ISPRS International Journal of Geo-Information

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21 pages, 4121 KiB

Open AccessArticle

Provenance in GIServices: A Semantic Web Approach

by Zhaoyan Wu, Hao Li and Peng Yue

ISPRS Int. J. Geo-Inf. 2023, 12(3), 118; https://doi.org/10.3390/ijgi12030118 - 9 Mar 2023

Viewed by 2051

Abstract

Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users [...] Read more.

Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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15 pages, 7074 KiB

Open AccessArticle

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

by Xiaochuang Yao, Guojiang Yu, Guoqing Li, Shuai Yan, Long Zhao and Dehai Zhu

ISPRS Int. J. Geo-Inf. 2023, 12(3), 89; https://doi.org/10.3390/ijgi12030089 - 23 Feb 2023

Cited by 4 | Viewed by 3007

Abstract

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in [...] Read more.

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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21 pages, 1534 KiB

Open AccessArticle

Geostatistics on Real-Time Geodata Streams—An Extended Spatiotemporal Moran’s I Index with Distributed Stream Processing Technologies

by Thomas Lemmerz, Stefan Herlé and Jörg Blankenbach

ISPRS Int. J. Geo-Inf. 2023, 12(3), 87; https://doi.org/10.3390/ijgi12030087 - 22 Feb 2023

Cited by 4 | Viewed by 1944

Abstract

The availability of geodata with high spatial and temporal resolution is increasing steadily. Often, these data are continuously generated by distributed sensor networks and provided as geodata streams. Geostatistical analysis methods, such as spatiotemporal autocorrelation, have thus far been applied primarily to historized [...] Read more.

The availability of geodata with high spatial and temporal resolution is increasing steadily. Often, these data are continuously generated by distributed sensor networks and provided as geodata streams. Geostatistical analysis methods, such as spatiotemporal autocorrelation, have thus far been applied primarily to historized data. As such, the advantages of continuous and up-to-date acquisition of geodata have not yet been transferred to the analysis phase. At the same time, open-source frameworks for distributed stream processing have been developed into powerful real-time data processing tools. In this paper a methodology is developed to apply analyses of spatiotemporal autocorrelation directly to geodata streams through a distributed streaming process using open-source software frameworks. For this purpose, we adapt the extended Moran’s I index for continuous and up-to-date computation, then apply it to simulated geospatial data streams of recorded taxi trip data. Various application scenarios for the developed methodology are tested and compared on a distributed computing cluster. The results show that the developed methodology can provide geostatistical analysis results in real time. This research demonstrates how modern datastream processing technologies have the potential to significantly advance the way geostatistical analysis can be performed and used in the future. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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15 pages, 1965 KiB

Open AccessArticle

Spatio-Temporal Transformer Recommender: Next Location Recommendation with Attention Mechanism by Mining the Spatio-Temporal Relationship between Visited Locations

by Shuqiang Xu, Qunying Huang and Zhiqiang Zou

ISPRS Int. J. Geo-Inf. 2023, 12(2), 79; https://doi.org/10.3390/ijgi12020079 - 20 Feb 2023

Cited by 3 | Viewed by 2440

Abstract

Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal [...] Read more.

Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal big data, i.e., the next point-of-interest (POI) recommendation. At present, while some advanced methods have been proposed for POI recommendation, existing work only leverages the temporal information of two consecutive LBSN check-ins. Specifically, these methods only focus on adjacent visit sequences but ignore non-contiguous visits, while these visits can be important in understanding the spatio-temporal correlation within the trajectory. In order to fully mine this non-contiguous visit information, we propose a multi-layer Spatio-Temporal deep learning attention model for POI recommendation, Spatio-Temporal Transformer Recommender (STTF-Recommender). To incorporate the spatio-temporal patterns, we encode the information in the user’s trajectory as latent representations into their embeddings before feeding them. To mine the spatio-temporal relationship between any two visited locations, we utilize the Transformer aggregation layer. To match the most plausible candidates from all locations, we develop on an attention matcher based on the attention mechanism. The STTF-Recommender was evaluated with two real-world datasets, and the findings showed that STTF improves at least 13.75% in the mean value of the Recall index at different scales compared with the state-of-the-art models. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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21 pages, 6003 KiB

Open AccessArticle

Classification of Seismaesthesia Information and Seismic Intensity Assessment by Multi-Model Coupling

by Qingzhou Lv, Wanzeng Liu, Ran Li, Hui Yang, Yuan Tao and Mengjiao Wang

ISPRS Int. J. Geo-Inf. 2023, 12(2), 46; https://doi.org/10.3390/ijgi12020046 - 31 Jan 2023

Cited by 2 | Viewed by 2063

Abstract

Earthquake disaster assessment is one of the most critical aspects in reducing earthquake disaster losses. However, traditional seismic intensity assessment methods are not effective in disaster-stricken areas with insufficient observation data. Social media data contain a large amount of disaster information with the [...] Read more.

Earthquake disaster assessment is one of the most critical aspects in reducing earthquake disaster losses. However, traditional seismic intensity assessment methods are not effective in disaster-stricken areas with insufficient observation data. Social media data contain a large amount of disaster information with the advantages of timeliness and multiple temporal-spatial scales, opening up a new channel for seismic intensity assessment. Based on the earthquake disaster information on the microblog platform obtained by the network technique, a multi-model coupled seismic intensity assessment method is proposed, which is based on the BERT-TextCNN model, constrained by the seismaesthesia intensity attenuation model, and supplemented by the method of ellipse-fitting inverse distance interpolation. Taking four earthquakes in Sichuan Province as examples, the earthquake intensity was evaluated in the affected areas from the perspective of seismaesthesia. The results show that (1) the microblog data contain a large amount of earthquake information, which can help identify the approximate scope of the disaster area; (2) the influences of the subjectivity and uneven spatial distribution of microblog data on the seismic intensity assessment can be reduced by using the seismaesthesia intensity attenuation model and the method of ellipse-fitting inverse distance interpolation; and (3) the accuracy of seismic intensity assessment based on the coupled model is 70.81%. Thus, the model has higher accuracy and universality. It can be used to assess seismic intensity in multiple regions and assist in the formulation of earthquake relief plans. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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25 pages, 6663 KiB

Open AccessArticle

Landslide Susceptibility Assessment in the Japanese Archipelago Based on a Landslide Distribution Map

by Masanori Kohno and Yuki Higuchi

ISPRS Int. J. Geo-Inf. 2023, 12(2), 37; https://doi.org/10.3390/ijgi12020037 - 22 Jan 2023

Cited by 10 | Viewed by 3512

Abstract

Though danger prediction and countermeasures for landslides are important, it is fundamentally difficult to take preventive measures in all areas susceptible to dangerous landslides. Therefore, it is necessary to perform landslide susceptibility mapping, extract slopes with high landslide hazard/risk, and prioritize locations for [...] Read more.

Though danger prediction and countermeasures for landslides are important, it is fundamentally difficult to take preventive measures in all areas susceptible to dangerous landslides. Therefore, it is necessary to perform landslide susceptibility mapping, extract slopes with high landslide hazard/risk, and prioritize locations for conducting investigations and countermeasures. In this study, landslide susceptibility mapping along the whole slope of the Japanese archipelago was performed using the analytical hierarchy process (AHP) method, and geographic information system analysis was conducted to extract the slope that had the same level of hazard/risk as areas where landslides occurred in the past, based on the ancient landslide topography in the Japanese archipelago. The evaluation factors used were elevation, slope angle, slope type, flow accumulation, geology, and vegetation. The landslide susceptibility of the slope was evaluated using the score accumulation from the AHP method for these evaluation factors. Based on the landslide susceptibility level (I to V), a landslide susceptibility map was prepared, and landslide susceptibility assessment in the Japanese archipelago was identified. The obtained landslide susceptibility map showed good correspondence with the landslide distribution, and correlated well with past landslide occurrences. This suggests that our method can be applied to the extraction of unstable slopes, and is effective for prioritizing and implementing preventative measures. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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20 pages, 5433 KiB

Open AccessEditor’s ChoiceArticle

Multi-GPU-Parallel and Tile-Based Kernel Density Estimation for Large-Scale Spatial Point Pattern Analysis

by Guiming Zhang and Jin Xu

ISPRS Int. J. Geo-Inf. 2023, 12(2), 31; https://doi.org/10.3390/ijgi12020031 - 18 Jan 2023

Cited by 5 | Viewed by 3024

Abstract

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one [...] Read more.

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one GPU for parallel computing. Additionally, it assumes that the input data can be held in GPU memory all at once for computation, which is unrealistic when conducting KDE analysis over large geographic areas at high resolution. This study develops a multi-GPU-parallel and tile-based KDE algorithm to overcome these limitations. It exploits multiple GPUs to speedup complex KDE computation by distributing computation across GPUs, and approaches density estimation with a tile-based strategy to bypass the memory bottleneck. Experiment results show that the parallel KDE algorithm running on multiple GPUs achieves significant speedups over running on a single GPU, and higher speedups are achieved on KDE tasks of a larger problem size. The tile-based strategy renders it feasible to estimate high-resolution density surfaces over large areas even on GPUs with only limited memory. Multi-GPU parallel computing and tile-based density estimation, while incurring very little computational overhead, effectively enable conducting KDE for large-scale spatial point pattern analysis on geospatial big data. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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17 pages, 9587 KiB

Open AccessArticle

Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree

by Song Chen, Fuhao Zhang, Zhiran Zhang, Siyi Yu, Agen Qiu, Shangqin Liu and Xizhi Zhao

ISPRS Int. J. Geo-Inf. 2023, 12(1), 24; https://doi.org/10.3390/ijgi12010024 - 14 Jan 2023

Cited by 1 | Viewed by 2574

Abstract

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called [...] Read more.

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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18 pages, 4374 KiB

Open AccessEditor’s ChoiceArticle

Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division

by Li Cai, Cong Sha, Jing He and Shaowen Yao

ISPRS Int. J. Geo-Inf. 2023, 12(1), 13; https://doi.org/10.3390/ijgi12010013 - 4 Jan 2023

Cited by 3 | Viewed by 2411

Abstract

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that [...] Read more.

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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17 pages, 2427 KiB

Open AccessArticle

A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features

by Xuhui Zeng, Shu Wang, Yunqiang Zhu, Mengfei Xu and Zhiqiang Zou

ISPRS Int. J. Geo-Inf. 2022, 11(12), 625; https://doi.org/10.3390/ijgi11120625 - 15 Dec 2022

Cited by 4 | Viewed by 2002

Abstract

The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar [...] Read more.

The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the ‘cold start’ problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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16 pages, 5508 KiB

Open AccessArticle

A Trajectory Big Data Storage Model Incorporating Partitioning and Spatio-Temporal Multidimensional Hierarchical Organization

by Zhixin Yao, Jianqin Zhang, Taizeng Li and Ying Ding

ISPRS Int. J. Geo-Inf. 2022, 11(12), 621; https://doi.org/10.3390/ijgi11120621 - 13 Dec 2022

Cited by 5 | Viewed by 2370

Abstract

Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the [...] Read more.

Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the above problems, this paper proposes a trajectory big data model that incorporates data partitioning and spatio-temporal multi-perspective hierarchical organization. At the spatial level, the model partitions the trajectory data based on the Hilbert curve and combines the pre-partitioning mechanism to solve the problems of hot writing and storage skewing of the distributed database HBase; at the temporal level, the model takes days as the organizational unit, finely encodes them into a minute system and then fuses the data partitioning to build spatio-temporal hybrid encoding to hierarchically organize the trajectory data and solve the problems of efficient storage and retrieval of trajectory data. The experimental results show that the model can effectively improve the storage and retrieval speed of trajectory big data under different orders of magnitude, while ensuring relatively stable writing and query speed, which can provide an efficient data model for trajectory big data mining and analysis. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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15 pages, 3549 KiB

Open AccessArticle

Spatial and Attribute Neural Network Weighted Regression for the Accurate Estimation of Spatial Non-Stationarity

by Sihan Ni, Zhongyi Wang, Yuanyuan Wang, Minghao Wang, Shuqi Li and Nan Wang

ISPRS Int. J. Geo-Inf. 2022, 11(12), 620; https://doi.org/10.3390/ijgi11120620 - 13 Dec 2022

Cited by 1 | Viewed by 2300

Abstract

Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to [...] Read more.

Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the “spatial-attribute” unified distance metric is useful, and that the SANNWR model showed the best performance. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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13 pages, 4546 KiB

Open AccessArticle

A Map Tile Data Access Model Based on the Jump Consistent Hash Algorithm

by Wei Wang, Xiaojing Yao and Jing Chen

ISPRS Int. J. Geo-Inf. 2022, 11(12), 608; https://doi.org/10.3390/ijgi11120608 - 6 Dec 2022

Cited by 3 | Viewed by 1972

Abstract

Tiled maps are one of the key GIS technologies used in the development and construction of WebGIS in the era of big data; there is an urgent need for high-performance tile map services hosted on big data GIS platforms. To address the current [...] Read more.

Tiled maps are one of the key GIS technologies used in the development and construction of WebGIS in the era of big data; there is an urgent need for high-performance tile map services hosted on big data GIS platforms. To address the current inefficiency of massive tile map data management and access, this paper proposes a massive tile map data access model that utilizes the jump consistent hash algorithm. Via the uniformity and consistency of a certain seed of a pseudo-random function, the algorithm can generate a storage slot for each tile data efficiently. By recording the slot information in the head of a row key, a uniform distribution of the tiles on the physical cluster nodes is achieved. This effectively solves the problem of hotspotting caused by the monotonicity of tile row keys in the data access process, thereby maximizing the random-access performance of a big data platform and greatly improving concurrent database access. Experiments show that this model can significantly improve the efficiency of tile map data access by more than 39% compared to a direct storage method, thereby confirming the model’s advantages in accessing massive tile map data on a big data GIS platform. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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22 pages, 9521 KiB

Open AccessArticle

Data-Driven Approach to Assess Street Safety: Large-Scale Analysis of the Microscopic Design

by Wanshu Wu, Jinhan Guo, Ziying Ma and Kai Zhao

ISPRS Int. J. Geo-Inf. 2022, 11(11), 537; https://doi.org/10.3390/ijgi11110537 - 27 Oct 2022

Cited by 9 | Viewed by 2581

Abstract

Safety is an important quality of street space that affects people’s psychological state and behavior in many ways. Previous large-scale assessment of street safety focuses more on social and physical factors and has less correlation with spatial design, especially the microscopic design. Limited [...] Read more.

Safety is an important quality of street space that affects people’s psychological state and behavior in many ways. Previous large-scale assessment of street safety focuses more on social and physical factors and has less correlation with spatial design, especially the microscopic design. Limited by data and methods, street safety assessment related to microscopic design is mostly conducted on the small scale. Based on multisource big data, this study conducts a data-driven approach to assess the safety of street microscope design on a large scale from the perspective of individual perception. An assessment system including four dimensions of walkability, spatial enclosure, visual permeability, and vitality is constructed, which reflects the individual perceptions of the street space. Intraclass correlation coefficient (ICC) and location-based service (LBS) data are used to verify the effectiveness of the assessment method. The results show that multisource big data can effectively measure the physical elements and design features of streets, reflecting street users’ perception of vision, function, architecture, and street form, as well as the spatial selectivity based on their judgment of safety. The measurement of multidimensional connotations and the fusion of multiple data mining technologies promote the accuracy and effectiveness of the assessment method. Street safety presents the spatial distribution of high-value aggregation and low-value dispersion. Street safety is relatively low in areas with a large scale, lack of street interface, large amount of transit traffic, and high-density vegetation cover. The proposed method and the obtained results can be a reference for humanized street design and sustainable urban traffic planning and management. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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20 pages, 11120 KiB

Open AccessArticle

Topological Access Methods for Spatial and Spatiotemporal Data

by Markus Wilhelm Jahn and Patrick Erik Bradley

ISPRS Int. J. Geo-Inf. 2022, 11(10), 533; https://doi.org/10.3390/ijgi11100533 - 20 Oct 2022

Cited by 2 | Viewed by 1990

Abstract

In order to perform topological queries on geographic data, it is necessary to first develop a topological access method (TOAM). Using the fact that any (incidence or other binary) relation produces a topology which includes the common usage of topology for spatial or [...] Read more.

In order to perform topological queries on geographic data, it is necessary to first develop a topological access method (TOAM). Using the fact that any (incidence or other binary) relation produces a topology which includes the common usage of topology for spatial or spatiotemporal data, here, such a TOAM is developed on the basis of the previously applied concept of Property Graph used in order to manage topological information in data of any dimension, whether time dependent or not. As a matter of fact, it is necessary to have a TOAM in order to query such a graph, and also to have data which are topologically consistent in a certain sense. While the rendering of topological consistency was the concern of previous work, here, the aim is to develop a methodology which builds on this concept. In the end, an experimental test of this approach on a small city model is performed. It turned out that the Euler characteristic, a well-known topological invariant, can be helpful for the initial data validation. Practically, this present theoretical work is seen to be necessary in view of future innovative applications, e.g., in the context of city model simulations, including distributed geo-processing. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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18 pages, 1366 KiB

Open AccessArticle

Adaptive Spatio-Temporal Query Strategies in Blockchain

by Haibo Chen and Daolei Liang

ISPRS Int. J. Geo-Inf. 2022, 11(7), 409; https://doi.org/10.3390/ijgi11070409 - 19 Jul 2022

Cited by 6 | Viewed by 2845

Abstract

In various applications of blockchain, how to index spatio-temporal data more efficiently has become a subject of continuous attention. The existing spatio-temporal data query in the blockchain is realized by adding additional external storage or fixed spatio-temporal index in the block, without considering [...] Read more.

In various applications of blockchain, how to index spatio-temporal data more efficiently has become a subject of continuous attention. The existing spatio-temporal data query in the blockchain is realized by adding additional external storage or fixed spatio-temporal index in the block, without considering the distribution of the spatio-temporal query itself and the proof performance accompanying the query. We propose an adaptive spatio-temporal blockchain index method, called Verkle AR*-tree, which adds the verification of time and location in the blockchain without additional storage and realizes the spatio-temporal index with an encrypted signature. Verkle AR*-tree further provides an adaptive algorithm, which adjusts the tree structure according to the historical query to produce the optimized index structure. The experimental results based on the pokeman dataset show that compared with the existing static spatio-temporal index, our method can effectively increase the performance of the spatio-temporal query and the spatio-temporal commitment in the blockchain. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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13 pages, 2441 KiB

Open AccessArticle

General Data Search Algorithms for Earth Simulation Systems with Cyclic Boundaries

by Yu Cao, Yan Chen, Huizan Wang, Xiaojiang Zhang and Wenjing Zhao

ISPRS Int. J. Geo-Inf. 2022, 11(7), 392; https://doi.org/10.3390/ijgi11070392 - 12 Jul 2022

Viewed by 1789

Abstract

Grid remapping is one of the most fundamental functions in Earth simulation systems, and is essentially a kind of data interpolation. The key to an efficient interpolation method is how to quickly find the relevant grid points required for interpolation. With the rise [...] Read more.

Grid remapping is one of the most fundamental functions in Earth simulation systems, and is essentially a kind of data interpolation. The key to an efficient interpolation method is how to quickly find the relevant grid points required for interpolation. With the rise of unstructured grid models, the demand for general and efficient interpolation search algorithms is becoming stronger and stronger. KD (K-dimensional) tree has proven to be effective in dealing with unstructured grids. However, it is unable to tackle the cyclic boundary conditions in Earth simulation systems, which restricts the application of KD tree. Taking the nearest neighbor search as an example, this paper introduces two new KD tree-based multi-dimensional data search methods, which break through the limitations of the original method with regards to the cyclic boundary. One method is based on target points duplication, and the other method is based on source points duplication. Their time complexity and space complexity are analyzed and verified by carefully designed experiments. The results show that the method based on target points duplication generally performs better than that based on source points duplication when the data are basically evenly distributed. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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22 pages, 6225 KiB

Open AccessArticle

Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

by Guijun Lai, Yuzhen Shang, Binbao He, Guanwei Zhao and Muzhuang Yang

ISPRS Int. J. Geo-Inf. 2022, 11(7), 377; https://doi.org/10.3390/ijgi11070377 - 7 Jul 2022

Cited by 6 | Viewed by 2502

Abstract

Characterizing the taxi travel network is of fundamental importance to our understanding of urban mobility, and could provide intellectual support for urban planning, traffic congestion, and even the spread of diseases. However, the research on the interaction network between urban functional area (UFA) [...] Read more.

Characterizing the taxi travel network is of fundamental importance to our understanding of urban mobility, and could provide intellectual support for urban planning, traffic congestion, and even the spread of diseases. However, the research on the interaction network between urban functional area (UFA) units are limited and worthy of notice. Therefore, this study has applied the taxi big data to construct a travel flow network for the exploration of spatial interaction relationships between different UFA units in Shenzhen, China. Our results suggested that taxi travel behavior was more active in UFA units dominated by functions, including residential, commercial, scenic, and greenspace during weekends, while more active in UFA units dominated by industrial function during weekdays. In terms of daily average volume, the characteristics of spatial interaction between the various UFA types during weekdays and weekends were similar. During the morning peak period, the sink areas were mainly distributed in Futian District and Nanshan District, while during the evening peak period, the sink areas were mainly distributed in the southern part of Yantian District, the southwestern part of Longgang District, and the eastern part of Luohu District. The average daily taxi mobility network during weekdays showed a spatial pattern of “dense in the west and north, sparse in the south and east”, exhibiting significant spatial unevenness. Compared with weekdays, the daily taxi mobility network during weekends was more dispersed and the differences in node sizes decreased, indicating that taxi travel destinations were more diverse. The pattern of communities was more consistent with the administrative division during weekdays, indicating that taxi trips are predominantly within the districts. Compared with weekdays, the community pattern of network during weekends was clearly different and more in line with the characteristics of a small world network. The findings can provide a better understanding of urban mobility characteristics in Shenzhen, and provide a reference for urban transportation planning and management. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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23 pages, 8574 KiB

Open AccessArticle

WaterSmart-GIS: A Web Application of a Data Assimilation Model to Support Irrigation Research and Decision Making

by Haoteng Zhao, Liping Di and Ziheng Sun

ISPRS Int. J. Geo-Inf. 2022, 11(5), 271; https://doi.org/10.3390/ijgi11050271 - 19 Apr 2022

Cited by 14 | Viewed by 3728

Abstract

Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge [...] Read more.

Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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15 pages, 3394 KiB

Open AccessArticle

A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network

by Haojian Liang, Shaohua Wang, Huilai Li, Huichun Ye and Yang Zhong

ISPRS Int. J. Geo-Inf. 2022, 11(5), 270; https://doi.org/10.3390/ijgi11050270 - 19 Apr 2022

Cited by 13 | Viewed by 3017

Abstract

The spatial optimization method between combinatorial optimization problems and GIS has many geographical applications. The p-center problem is a classic NP-hard location modeling problem, which has essential applications in many real-world scenarios, such as urban facility locations (ambulances, fire stations, pipelines maintenance centers, [...] Read more.

The spatial optimization method between combinatorial optimization problems and GIS has many geographical applications. The p-center problem is a classic NP-hard location modeling problem, which has essential applications in many real-world scenarios, such as urban facility locations (ambulances, fire stations, pipelines maintenance centers, police stations, etc.). This study implements two methods to solve this problem: an exact algorithm and an approximate algorithm. Exact algorithms can get the optimal solution to the problem, but they are inefficient and time-consuming. The approximate algorithm can give the sub-optimal solution of the problem in polynomial time, which has high efficiency, but the accuracy of the solution is closely related to the initialization center point. We propose a new paradigm that combines a graph convolution network and greedy algorithm to solve the p-center problem through direct training and realize that the efficiency is faster than the exact algorithm. The accuracy is superior to the heuristic algorithm. We generate a large amount of p-center problems by the Erdos–Renyi graph, which can generate instances in many real problems. Experiments show that our method can compromise between time and accuracy and affect the solution of p-center problems. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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17 pages, 1847 KiB

Open AccessArticle

A Distributed Hybrid Indexing for Continuous KNN Query Processing over Moving Objects

by Imene Bareche and Ying Xia

ISPRS Int. J. Geo-Inf. 2022, 11(4), 264; https://doi.org/10.3390/ijgi11040264 - 17 Apr 2022

Cited by 7 | Viewed by 2790

Abstract

The magnitude of highly dynamic spatial data is expanding rapidly due to the instantaneous evolution of mobile technology, resulting in challenges for continuous queries. We propose a novel indexing approach model, namely, the Velocity SpatioTemporal indexing approach (VeST), for continuous queries, mainly Continuous [...] Read more.

The magnitude of highly dynamic spatial data is expanding rapidly due to the instantaneous evolution of mobile technology, resulting in challenges for continuous queries. We propose a novel indexing approach model, namely, the Velocity SpatioTemporal indexing approach (VeST), for continuous queries, mainly Continuous K-nearest Neighbor (CKNN) and continuous range queries using Apache Spark. The proposed structure is based on a selective velocity partitioning method, i.e., since different objects have varying speeds, we divide the objects into two sets according to the actual mean speed we calculate before building the index and accessing data. Then the adopted indexing structure base unit comprises a nonoverlapping R-tree and a two dimension grid. The tree divides the space into nonoverlapping minimum bounding regions that point to the grids. Then, the uniform grid stores the object data of leaf nodes. This access method reduces the update cost and improves response time and query precision. In order to enhance performances for large-scale processing, we design a compact multilayer index structure on a distributed setting and propose a CKNN search algorithm for accurate results using a candidate cell identification process. We provide a comprehensive vision of our indexing model and the adopted query technique. The simulation results show that for query intervals of 100, the proposed approach is 13.59 times faster than the traditional approach, and the average time of the VeST approach is less than 0.005 for all query intervals. This proposed method improves response time and query precision. The precision of the VeST algorithm is almost equal to 100% regardless of the length of the query interval. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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17 pages, 9646 KiB

Open AccessArticle

Crowd Anomaly Detection via Spatial Constraints and Meaningful Perturbation

by Jiangfan Feng, Dini Wang and Li Zhang

ISPRS Int. J. Geo-Inf. 2022, 11(3), 205; https://doi.org/10.3390/ijgi11030205 - 18 Mar 2022

Cited by 7 | Viewed by 4007

Abstract

Crowd anomaly detection is a practical and challenging problem to computer vision and VideoGIS due to abnormal events’ rare and diverse nature. Consequently, traditional methods rely on low-level reconstruction in a single image space, easily affected by unimportant pixels or sudden variations. In [...] Read more.

Crowd anomaly detection is a practical and challenging problem to computer vision and VideoGIS due to abnormal events’ rare and diverse nature. Consequently, traditional methods rely on low-level reconstruction in a single image space, easily affected by unimportant pixels or sudden variations. In addition, real-time detection for crowd anomaly detection is challenging, and localization of anomalies requires other supervision. We present a new detection approach to learn spatiotemporal features with the spatial constraints of a still dynamic image. First, a lightweight spatiotemporal autoencoder has been proposed, capable of real-time image reconstruction. Second, we offer a dynamic network to obtain a compact representation of video frames in motion, reducing false-positive anomaly alerts by spatial constraints. In addition, we adopt the perturbation visual interpretation method for anomaly visualization and localization to improve the credibility of the results. In experiments, our results provide competitive performance across various scenarios. Besides, our approach can process 52.9–63.4 fps in anomaly detection, making it practical for crowd anomaly detection in video surveillance. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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14 pages, 1527 KiB

Open AccessArticle

Spatial-Temporal Diffusion Convolutional Network: A Novel Framework for Taxi Demand Forecasting

by Aling Luo, Boyi Shangguan, Can Yang, Fan Gao, Zhe Fang and Dayu Yu

ISPRS Int. J. Geo-Inf. 2022, 11(3), 193; https://doi.org/10.3390/ijgi11030193 - 13 Mar 2022

Cited by 8 | Viewed by 4247

Abstract

Taxi demand forecasting plays an important role in ride-hailing services. Accurate taxi demand forecasting can assist taxi companies in pre-allocating taxis, improving vehicle utilization, reducing waiting time, and alleviating traffic congestion. It is a challenging task due to the highly non-linear and complicated [...] Read more.

Taxi demand forecasting plays an important role in ride-hailing services. Accurate taxi demand forecasting can assist taxi companies in pre-allocating taxis, improving vehicle utilization, reducing waiting time, and alleviating traffic congestion. It is a challenging task due to the highly non-linear and complicated spatial-temporal patterns of the taxi data. Most of the existing taxi demand forecasting methods lack the ability to capture the dynamic spatial-temporal dependencies among regions. They either fail to consider the limitations of Graph Neural Networks or do not efficiently capture the long-term temporal dependencies. In this paper, we propose a Spatial-Temporal Diffusion Convolutional Network (ST-DCN) for taxi demand forecasting. The dynamic spatial dependencies are efficiently captured through a two-phase graph diffusion convolutional network where the attention mechanism is introduced. Moreover, a novel temporal convolution module is designed to learn various ranges of temporal dependencies, including recent, daily, and weekly periods. Inside the module, convolution layers are stacked to handle very long sequences. Experimental results on two large-scale real-world taxi datasets from New York City (NYC) and Chengdu demonstrate that our method significantly outperforms seven state-of-the-art baseline methods. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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16 pages, 3968 KiB

Open AccessArticle

B-GPS: Blockchain-Based Global Positioning System for Improved Data Integrity and Reliability

by Seunghyeon Lee, Hong-Woo Seok, Ki-rim Lee and Hoh Peter In

ISPRS Int. J. Geo-Inf. 2022, 11(3), 186; https://doi.org/10.3390/ijgi11030186 - 9 Mar 2022

Cited by 6 | Viewed by 6526

Abstract

When surveying national reference points using a global positioning system (GPS), appropriate work regulations pertaining to the surveying time must be observed. However, such data can be modified easily, so identifying non-compliance with work regulations and forgeries is challenging. If such incidents occur [...] Read more.

When surveying national reference points using a global positioning system (GPS), appropriate work regulations pertaining to the surveying time must be observed. However, such data can be modified easily, so identifying non-compliance with work regulations and forgeries is challenging. If such incidents occur in cadastral surveys, it may result in financial damages to stakeholders, such as citizens and the state. Therefore, it is necessary to improve the reliability by ensuring the integrity of the GPS positioning data and allowing anyone to track them. In this study, a prototype system was developed to record GPS data and the corrections generated during survey processes using the Ethereum blockchain network. Blockchain is a distributed ledger system that prevents the manipulation of uploaded data without the need for a centralized institution by allowing anyone to check the data. Unlike in the past, the proposed system improves the data integrity and reliability for the entire survey process through blockchain, thereby ensuring transparency of the checks using smart contract addresses. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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27 pages, 5592 KiB

Open AccessArticle

Positioning Localities for Vague Spatial Location Description: A Supervaluation Semantics Approach

by Peng Ye, Xueying Zhang, Chunju Zhang and Yulong Dang

ISPRS Int. J. Geo-Inf. 2022, 11(1), 68; https://doi.org/10.3390/ijgi11010068 - 15 Jan 2022

Cited by 3 | Viewed by 4183

Abstract

In the big data era, spatial positioning based on location description is the foundation to the intelligent transformation of location-based-services. To solve the problem of vagueness in location description in different contexts, this paper proposes a positioning method based on supervaluation semantics. Firstly, [...] Read more.

In the big data era, spatial positioning based on location description is the foundation to the intelligent transformation of location-based-services. To solve the problem of vagueness in location description in different contexts, this paper proposes a positioning method based on supervaluation semantics. Firstly, through combing the laws of human spatial cognition, the types of elements that people pay attention to in location description are clarified. On this basis, the source of vagueness in the location description and its embodiment in the expression form of each element are analyzed from multiple levels. Secondly, the positioning model is constructed from the following three aspects: spatial object, distance relation and direction relation. The contexts of multiple location description are super-valued, respectively, while the threshold of observations is obtained from the context semantics. Thus, the precisification of location description is realized for positioning. Thirdly, a question-answering system is designed to the collect contexts of location description, and a case study on the method is conducted. The case can verify the transformation of a set of users’ viewpoints on spatial cognition into the real-world spatial scope, to realize the representation of vague location description in the geographic information system. The result shows that the method proposed in the paper breaks through the traditional vagueness modeling, which only focuses on spatial relationship, and enhances the interpretability of semantics of vague location description. Moreover, supervaluation semantics can obtain the precisification results of vague location description in different situations, and the positioning localities are more suitable to individual subjective cognition. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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24 pages, 10075 KiB

Open AccessArticle

Integration of Web Processing Services with Workflow-Based Scientific Applications for Solving Environmental Monitoring Problems

by Alexander Feoktistov, Sergey Gorsky, Roman Kostromin, Roman Fedorov and Igor Bychkov

ISPRS Int. J. Geo-Inf. 2022, 11(1), 8; https://doi.org/10.3390/ijgi11010008 - 28 Dec 2021

Cited by 4 | Viewed by 2568

Abstract

Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the [...] Read more.

Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the utilization of high-performance computing. To this end, the paper addresses a new approach to automation of implementing resource-intensive computational operations of web processing services in a heterogeneous distributed computing environment. To implement such an operation, we develop a workflow-based scientific application executed under the control of a multi-agent system. Agents represent heterogeneous resources of the environment and distribute the computational load among themselves. Software development is realized in the Orlando Tools framework, which we apply to creating and operating problem-oriented applications. The advantages of the proposed approach are in integrating geographic information services and high-performance computing tools, as well as in increasing computation speedup, balancing computational load, and improving the efficiency of resource use in the heterogeneous distributed computing environment. These advantages are shown in analyzing multidimensional time series. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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14 pages, 789 KiB

Open AccessArticle

The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding

by Zheren Yan, Can Yang, Lei Hu, Jing Zhao, Liangcun Jiang and Jianya Gong

ISPRS Int. J. Geo-Inf. 2021, 10(9), 572; https://doi.org/10.3390/ijgi10090572 - 24 Aug 2021

Cited by 8 | Viewed by 4355

Abstract

Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. [...] Read more.

Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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35 pages, 6832 KiB

Open AccessArticle

A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions

by Jean-Paul Kasprzyk and Guénaël Devillet

ISPRS Int. J. Geo-Inf. 2021, 10(2), 87; https://doi.org/10.3390/ijgi10020087 - 19 Feb 2021

Cited by 5 | Viewed by 4650

Abstract

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. [...] Read more.

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called “Racines”, gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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22 pages, 2985 KiB

Open AccessArticle

A Blockchain Solution for Securing Real Property Transactions: A Case Study for Serbia

by Goran Sladić, Branko Milosavljević, Siniša Nikolić, Dubravka Sladić and Aleksandra Radulović

ISPRS Int. J. Geo-Inf. 2021, 10(1), 35; https://doi.org/10.3390/ijgi10010035 - 15 Jan 2021

Cited by 48 | Viewed by 7830

Abstract

The origins of digital money and blockchain technology goes back to the 1980s, but in the last decade, the blockchain technology gained large popularity in the financial sector with the appearance of cryptocurrencies such as Bitcoin. However, recently, many other fields of application [...] Read more.

The origins of digital money and blockchain technology goes back to the 1980s, but in the last decade, the blockchain technology gained large popularity in the financial sector with the appearance of cryptocurrencies such as Bitcoin. However, recently, many other fields of application have been recognized, particularly with the development of smart contracts. Among them is the possible application of blockchain technology in the domain of land administration, mostly as a tool for transparency in the developing countries and means to fight corruption. However, developed countries also find interest in launching pilot projects to test their applicability in land administration domain for reasons such as to increase the speed and reduce costs of the real property transactions through a more secure environment. In this paper, we analyse how transactions are handled in Serbian land administration and how this process may be supported by modern ledger technologies such as blockchain. In order to analyse how blockchain could be implemented to support transactions in land information systems (LIS), it is necessary to understand cadastral processes and transactions in LIS, as well as legislative and organizational aspects of LIS. Transactions in cadastre comprise many actors and utilize both alphanumeric (descriptive or legal) data and geospatial data about property boundaries on the cadastral map. Based on the determined requirements for the blockchain-based LIS, we propose a system architecture for its implementation. Such a system keeps track of transactions in LIS in an immutable and tamper-proof manner to increase the security of the system and consequently increase the speed of transactions, efficiency, and data integrity without a significant impact on the existing laws and regulations. The system is anticipated as a permissioned public blockchain implemented on top of the Ethereum network. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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17 pages, 1010 KiB

Open AccessArticle

An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase

by Quan Xiong, Xiaodong Zhang, Wei Liu, Sijing Ye, Zhenbo Du, Diyou Liu, Dehai Zhu, Zhe Liu and Xiaochuang Yao

ISPRS Int. J. Geo-Inf. 2020, 9(11), 625; https://doi.org/10.3390/ijgi9110625 - 25 Oct 2020

Cited by 7 | Viewed by 3319

Abstract

Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their [...] Read more.

Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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32 pages, 23484 KiB

Open AccessArticle

An Illumination Insensitive Descriptor Combining the CSLBP Features for Street View Images in Augmented Reality: Experimental Studies

by Zejun Xiang, Ronghua Yang, Chang Deng, Mingxing Teng, Mengkun She and Degui Teng

ISPRS Int. J. Geo-Inf. 2020, 9(6), 362; https://doi.org/10.3390/ijgi9060362 - 1 Jun 2020

Cited by 2 | Viewed by 2685

Abstract

The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric [...] Read more.

The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric local binary pattern (CS-LBP) into a common feature description framework. This proposed descriptor can be used to improve the performance of eight commonly used feature-matching algorithms, e.g., SIFT, SURF, DAISY, BRISK, ORB, FREAK, KAZE, and AKAZE. We perform the experiments on five street view image sequences with different illumination changes. By comparing with the performance of eight original algorithms, the evaluation results show that our improved algorithms can improve the matching accuracy of street view images with changing illumination. Further, the time consumption only increases a little. Therefore, our combined descriptors are much more robust against light changes to satisfy the high precision requirement of augmented reality (AR) system. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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Graphical abstract

20 pages, 3878 KiB

Open AccessArticle

Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows

by Ziheng Sun, Liping Di, Annie Burgess, Jason A. Tullis and Andrew B. Magill

ISPRS Int. J. Geo-Inf. 2020, 9(2), 119; https://doi.org/10.3390/ijgi9020119 - 21 Feb 2020

Cited by 24 | Viewed by 6902

Abstract

AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing [...] Read more.

AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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16 pages, 6487 KiB

Open AccessArticle

A Universal Generating Algorithm of the Polyhedral Discrete Grid Based on Unit Duplication

by Li Meng, Xiaochong Tong, Shuaibo Fan, Chengqi Cheng, Bo Chen, Weiming Yang and Kaihua Hou

ISPRS Int. J. Geo-Inf. 2019, 8(3), 146; https://doi.org/10.3390/ijgi8030146 - 19 Mar 2019

Cited by 4 | Viewed by 4270

Abstract

Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region [...] Read more.

Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region restriction”. First, the grid coordinate system and the corresponding spatial rectangular coordinate system are established to determine the rectangular coordinates of any grid cell node. Then, the type of the subdivision grid system to be calculated is determined to identify the three key factors affecting the grid types, which are the position of the starting point, the length of the starting edge, and the direction of the starting edge. On this basis, the effective boundary of a multiscale grid can be determined and the grid coordinates of a multiscale grid can be obtained. A one-to-one correspondence between the multiscale grids and subdivision types can be established. Through the appropriate rotation, translation and scaling of the multiscale grid, the node coordinates of a single triangular grid system are calculated, and the relationships between the nodes of different levels are established. Finally, this paper takes a hexagonal grid as an example to carry out the experiment verifications by converting a single triangular grid system (plane) directly to a single triangular grid with a positive icosahedral surface to generate a positive icosahedral surface grid. The experimental results show that the algorithm has good universality and can generate the multiscale grid of an arbitrary grid configuration by adjusting the corresponding starting transformation parameters. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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14 pages, 1859 KiB

Open AccessArticle

Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data

by Mengyu Ma, Ye Wu, Luo Chen, Jun Li and Ning Jing

ISPRS Int. J. Geo-Inf. 2019, 8(1), 21; https://doi.org/10.3390/ijgi8010021 - 10 Jan 2019

Cited by 13 | Viewed by 5150

Abstract

Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of [...] Read more.

Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn:8080/hibo). Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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Review

Jump to: Research, Other

23 pages, 1214 KiB

Open AccessReview

Spatial Decision Support Systems with Automated Machine Learning: A Review

by Richard Wen and Songnian Li

ISPRS Int. J. Geo-Inf. 2023, 12(1), 12; https://doi.org/10.3390/ijgi12010012 - 30 Dec 2022

Cited by 4 | Viewed by 5696

Abstract

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and [...] Read more.

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields—bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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21 pages, 4791 KiB

Open AccessTechnical Note

Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm

by Tengfei Yang, Jibo Xie, Guoqing Li, Naixia Mou, Cuiju Chen, Jing Zhao, Zhan Liu and Zhenyu Lin

ISPRS Int. J. Geo-Inf. 2020, 9(2), 136; https://doi.org/10.3390/ijgi9020136 - 24 Feb 2020

Cited by 11 | Viewed by 4119

Abstract

The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile [...] Read more.

The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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