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  • Cartography and geographic base data  (17)
  • 1
    Online Resource
    Online Resource
    Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng, China
    Informa UK Limited ; 2019
    In:  GIScience & Remote Sensing Vol. 56, No. 2 ( 2019-02-17), p. 282-308
    Details
    In: GIScience & Remote Sensing, Informa UK Limited, Vol. 56, No. 2 ( 2019-02-17), p. 282-308
    Type of Medium: Online Resource
    ISSN: 1548-1603 , 1943-7226
    URL: Article
    DOI: 10.1080/15481603.2018.1507074
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2209042-3
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  • 2
    Online Resource
    Online Resource
    Understanding China’s urban functional patterns at the county scale by using time-series social media data
    Informa UK Limited ; 2022
    In:  Journal of Spatial Science
    Details
    In: Journal of Spatial Science, Informa UK Limited
    Type of Medium: Online Resource
    ISSN: 1449-8596 , 1836-5655
    URL: Article
    DOI: 10.1080/14498596.2022.2125095
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2216491-1
    SSG: 14,1
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  • 3
    Online Resource
    Online Resource
    Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 2 ( 2020-02-10), p. 106-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 2 ( 2020-02-10), p. 106-
    Abstract: Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai’s homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9020106
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 4
    Online Resource
    Online Resource
    A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 8 ( 2018-08-17), p. 330-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 8 ( 2018-08-17), p. 330-
    Abstract: A segmented processing approach of eigenvector spatial filtering (ESF) regression is proposed to detect the relationship between NDVI and its environmental factors like DEM, precipitation, relative humidity, precipitation days, soil organic carbon, and soil base saturation in central China. An optimum size of 32 × 32 is selected through experiments as the basic unit for image segmentation to resolve the large datasets to smaller ones that can be performed in parallel and processed more efficiently. The eigenvectors from the spatial weights matrix (SWM) of each segmented image block are selected as synthetic proxy variables accounting for the spatial effects and aggregated to construct a global ESF regression model. Results show precipitation and humidity are more influential than other factors and spatial autocorrelation plays a vital role in vegetation cover in central China. Despite the increase in model complexity; the parallel ESF regression model performs best across all performance criteria compared to the ordinary least squared linear regression (OLS) and spatial autoregressive (SAR) models. The proposed parallel ESF approach overcomes the computational barrier for large data sets and is very promising in applying spatial regression modeling to a wide range of real world problem solving and forecasting.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7080330
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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  • 5
    Online Resource
    Online Resource
    Mobility Modes Awareness from Trajectories Based on Clustering and a Convolutional Neural Network
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 5 ( 2019-05-07), p. 208-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 5 ( 2019-05-07), p. 208-
    Abstract: Massive trajectory data generated by ubiquitous position acquisition technology are valuable for knowledge discovery. The study of trajectory mining that converts knowledge into decision support becomes appealing. Mobility modes awareness is one of the most important aspects of trajectory mining. It contributes to land use planning, intelligent transportation, anomaly events prevention, etc. To achieve better comprehension of mobility modes, we propose a method to integrate the issues of mobility modes discovery and mobility modes identification together. Firstly, route patterns of trajectories were mined based on unsupervised origin and destination (OD) points clustering. After the combination of route patterns and travel activity information, different mobility modes existing in history trajectories were discovered. Then a convolutional neural network (CNN)-based method was proposed to identify the mobility modes of newly emerging trajectories. The labeled history trajectory data were utilized to train the identification model. Moreover, in this approach, we introduced a mobility-based trajectory structure as the input of the identification model. This method was evaluated with a real-world maritime trajectory dataset. The experiment results indicated the excellence of this method. The mobility modes discovered by our method were clearly distinguishable from each other and the identification accuracy was higher compared with other techniques.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8050208
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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  • 6
    Online Resource
    Online Resource
    Evaluation of spatio-temporal variations of FVC and its relationship with climate change using GEE and Landsat images in Ganjiang River Basin
    Informa UK Limited ; 2022
    In:  Geocarto International Vol. 37, No. 26 ( 2022-12-13), p. 13658-13688
    Details
    In: Geocarto International, Informa UK Limited, Vol. 37, No. 26 ( 2022-12-13), p. 13658-13688
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2022.2082551
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2109550-4
    SSG: 14
    SSG: 14,1
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  • 7
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    Online Resource
    Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 2 ( 2021-02-17), p. 79-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 2 ( 2021-02-17), p. 79-
    Abstract: Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10020079
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 8
    Online Resource
    Online Resource
    GroupSeeker: An Applicable Framework for Travel Companion Discovery from Vast Trajectory Data
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 6 ( 2020-06-20), p. 404-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 6 ( 2020-06-20), p. 404-
    Abstract: The popularity of mobile locate-enabled devices and Location Based Service (LBS) generates massive spatio-temporal data every day. Due to the close relationship between behavior patterns and movement trajectory, trajectory data mining has been applied in numerous fields to find the behavior pattern. Among them, discovering traveling companions is one of the most fundamental techniques in these areas. This paper proposes a flexible framework named GroupSeeker for discovering traveling companions in vast real-world trajectory data. In the real-world data resource, it is significant to avoid the companion candidate omitting problem happening in the time-snapshot-slicing-based method. These methods do not work well with the sparse real-world data, which is caused by the equipment sampling failure or manual intervention. In this paper, a 5-stage framework including Data Preprocessing, Spatio-temporal Clustering, Candidate Voting, Pseudo-companion Filtering, and Group Merging is proposed to discover traveling companions. The framework even works well when there is a long time span during several days. The experiments result on two real-world data sources which offer massive amount of data subsets with different scale and different sampling frequencies show the effective and robustness of this framework. Besides, the proposed framework has a higher-efficiency performing when discovering satisfying companions over a long-term period.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9060404
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 9
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    Online Resource
    Impact of aerosols on terrestrial gross primary productivity in North China using an improved boreal ecosystem productivity simulator with satellite-based aerosol optical depth
    Informa UK Limited ; 2020
    In:  GIScience & Remote Sensing Vol. 57, No. 2 ( 2020-02-17), p. 258-270
    Details
    In: GIScience & Remote Sensing, Informa UK Limited, Vol. 57, No. 2 ( 2020-02-17), p. 258-270
    Type of Medium: Online Resource
    ISSN: 1548-1603 , 1943-7226
    URL: Article
    DOI: 10.1080/15481603.2019.1682237
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2209042-3
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  • 10
    Online Resource
    Online Resource
    Studies on Three-Dimensional (3D) Modeling of UAV Oblique Imagery with the Aid of Loop-Shooting
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 9 ( 2018-08-27), p. 356-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 9 ( 2018-08-27), p. 356-
    Abstract: Oblique imagery obtained from an Unmanned Aerial Vehicle (UAV) has been widely applied to large-scale three-dimensional (3D) reconstruction; however, the problems of partially missing model details caused by such factors as occlusion, distortion, and airflow, are still not well resolved. In this paper, a loop-shooting-aided technology is used to solve the problem of details loss in the 3D model. The use of loop-shooting technology can effectively compensate for losses caused by occlusion, distortion, or airflow during UAV flight and enhance the 3D model details in large scene- modeling applications. Applying this technology involves two key steps. First, based on the 3D modeling construction process, the missing details of the modeling scene are found. Second, using loop-shooting image sets as the data source, incremental iterative fitting based on aerotriangulation theory is used to compensate for the missing details in the 3D model. The experimental data used in this paper were collected from Yunnan Normal University, Chenggong District, Kunming City, Yunnan Province, China. The experiments demonstrate that loop-shooting significantly improves the aerotriangulation accuracy and effectively compensates for defects during 3D large-scale model reconstruction. In standard-scale distance tests, the average relative accuracy of our modeling algorithm reached 99.87% and achieved good results. Therefore, this technique not only optimizes the model accuracy and ensures model integrity, but also simplifies the process of refining the 3D model. This study can be useful as a reference and as scientific guidance in large-scale stereo measurements, cultural heritage protection, and smart city construction.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7090356
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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