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  • Cartography and geographic base data  (35)
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  • Cartography and geographic base data  (35)
  • 1
    Online Resource
    Online Resource
    Assessment of Multiple GNSS Real-Time SSR Products from Different Analysis Centers
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 3 ( 2018-03-08), p. 85-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 3 ( 2018-03-08), p. 85-
    Abstract: The real-time State Space Representation (SSR) product of the GNSS (Global Navigation Satellite System) orbit and clock is one of the most essential corrections for real-time precise point positioning (PPP). In this work, the performance of current SSR products from eight analysis centers were assessed by comparing it with the final product and the accuracy of real-time PPP. Numerical results showed that (1) the accuracies of the GPS SSR product were better than 8 cm for the satellite orbit and 0.3 ns for the satellite clock; (2) the accuracies of the GLONASS (GLObalnaya NAvigatsionnaya Sputnikovaya Sistema) SSR product were better than 10 cm for orbit RMS (Root Mean Square) and 0.6 ns for clock STD (Standard Deviation); and (3) the accuracies of the BDS (BeiDou Navigation Satellite System) and Galileo SSR products from CLK93 were about 14.54 and 4.42 cm for the orbit RMS and 0.32 and 0.18 ns for the clock STD, respectively. The simulated kinematic PPP results obtained using the SSR products from CLK93 and CLK51 performed better than those using other SSR products; and the accuracy of PPP based on all products was better than 6 and 10 cm in the horizontal and vertical directions, respectively. The real-time kinematic PPP experiment carried out in Beijing, Tianjin, and Shijiazhuang, China indicated that the SSR product CLK93 from Centre National d’Etudes Spatiales (CNES) had a better performance than CAS01. Moreover, the PPP with GPS + BDS dual systems had a higher accuracy than those with only a GPS single system.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7030085
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Eco-geological environment quality assessment in a mining city: a case study of Jiangxia District, Wuhan City
    Informa UK Limited ; 2023
    In:  Geocarto International Vol. 38, No. 1 ( 2023-12-31)
    Details
    In: Geocarto International, Informa UK Limited, Vol. 38, No. 1 ( 2023-12-31)
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2023.2245795
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2109550-4
    detail.hit.zdb_id: 56440-0
    SSG: 14
    SSG: 14,1
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  • 3
    Online Resource
    Online Resource
    Enhanced shuffle attention network based on visual working mechanism for high-resolution remote sensing image classification
    Informa UK Limited ; 2022
    In:  Geocarto International Vol. 37, No. 27 ( 2022-12-13), p. 18731-18766
    Details
    In: Geocarto International, Informa UK Limited, Vol. 37, No. 27 ( 2022-12-13), p. 18731-18766
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2022.2143912
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2109550-4
    detail.hit.zdb_id: 56440-0
    SSG: 14
    SSG: 14,1
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 2 ( 2022-01-18), p. 72-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 2 ( 2022-01-18), p. 72-
    Abstract: Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11020072
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Site Selection of Retail Shops Based on Spatial Accessibility and Hybrid BP Neural Network
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 6 ( 2018-05-29), p. 202-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 6 ( 2018-05-29), p. 202-
    Abstract: The increase of consumer income has resulted in the rapid development of the retail industry in China, which provides high market potential for retail companies worldwide. However, site selection for retail shops has been a confusing business issue in practical business decisions. In this study, a two-step hybrid model in site selection for small retail shops was proposed. The two steps were spatial accessibility evaluation and market potential estimation. The spatial accessibility of target regions was evaluated based on the improved gravity model to determine regions that lack retail shops. Then, a PCA (principal component analysis)–BP (backpropagation network) model was established to estimate the market potential in the target regions. The two-step model could determine sites with the most market potential and low competition. We conducted the experiment in Guiyang, China and considered 18 socioeconomic factors to make the site selection convincing. Through the experiment, 42 locations were determined with high business value; the locations were recommended to the new retail shops. The accuracy of the PCA–BP model was then proven satisfactory by comparing it with other regression methods. The proposed model could guide retail chains in enhancing business location planning and formulating regional development policies.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7060202
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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  • 6
    Online Resource
    Online Resource
    Analysis of Urban Vitality in Nanjing Based on a Plot Boundary-Based Neural Network Weighted Regression Model
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 12 ( 2022-12-15), p. 624-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 12 ( 2022-12-15), p. 624-
    Abstract: As a representative indicator for the level and sustainability of urban development, urban vitality has been widely used to assess the quality of urban development. However, urban vitality is too blurry to be accurately quantified and is often limited to a particular type of expression of vitality. Current regression models often fail to accurately express the spatial heterogeneity of vibrancy and drivers. Therefore, this paper took Nanjing as the study area and quantified the social, cultural, and economic vitality indicators based on mobile phone data, POI data, and night-light remote sensing data. We also mapped the spatial distribution of comprehensive urban vitality using an improved entropy method and analyzed the spatial heterogeneity of urban vitality and its influencing factors using a plot boundary-based neural network weighted regression (PBNNWR). The results show: (1) The comprehensive vitality in Nanjing is distributed in a “three-center” pattern with one large and two small centers; (2) PBNNWR can be used to investigate the local regression relationships among the driving factors and urban vitality, and the fitting accuracy (95.6%) of comprehensive vitality in weekdays is higher than that of ordinary least squares regression (OLS) (65.9%), geographically weighted regression (GWR) (89.9%), and geographic neural network weighted regression (GNNWR) (89.5%) models; (3) House price, functional diversity, building density, metro station accessibility, and residential facility density are factors that significantly affect urban vitality. The study’s findings can provide theoretical guidance for optimizing the urban spatial layout, resource allocation, and targeted planning strategies for areas with different vitality values.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11120624
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 7
    Online Resource
    Online Resource
    An Artificial Bee Colony-based Algorithm to Automatically Create Colour Schemes for Geovisualizations
    Informa UK Limited ; 2019
    In:  The Cartographic Journal Vol. 56, No. 2 ( 2019-04-03), p. 161-174
    Details
    In: The Cartographic Journal, Informa UK Limited, Vol. 56, No. 2 ( 2019-04-03), p. 161-174
    Type of Medium: Online Resource
    ISSN: 0008-7041 , 1743-2774
    URL: Article
    DOI: 10.1080/00087041.2018.1507182
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2019
    detail.hit.zdb_id: 417437-9
    detail.hit.zdb_id: 2113652-X
    SSG: 14,1
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    Online Resource
  • 8
    Online Resource
    Online Resource
    Simulation of Land-Use Changes Using the Partitioned ANN-CA Model and Considering the Influence of Land-Use Change Frequency
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 5 ( 2021-05-18), p. 346-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 5 ( 2021-05-18), p. 346-
    Abstract: Land-use change is a typical geographic evolutionary process characterized by spatial heterogeneity. As such, the driving factors, conversion rules, and rate of change vary for different regions around the world. However, most cellular automata (CA) models use the same transition rules for all cells in the model space when simulating land-use change. Thus, spatial heterogeneity change is ignored in the model, which means that these models are prone to over- or under simulation, resulting in a large deviation from reality. An effective means of accounting for the influence of spatial heterogeneity on the quality of the CA model is to establish a partitioned model based on cellular space partitioning. This study established a partitioned, dual-constrained CA model using the area-weighted frequency of land-use change (AWFLUC) to capture its spatial heterogeneity. This model was used to simulate the land-use evolution of the Dianchi Lake watershed. First, the CA space was divided into subzones using a dual-constrained spatial clustering method. Second, an artificial neural network (ANN) was used to automatically acquire conversion rules to construct an ANN-CA model of land-use change. Finally, land-use changes were simulated using the ANN-CA model based on data from 2006 to 2016, and model reliability was validated. The experimental results showed that compared with the non-partitioned CA model, the partitioned counterpart was able to improve the accuracy of land-use change simulation significantly. Furthermore, AWFLUC is an important indicator of the spatial heterogeneity of land-use change. The shapes of the division spaces were more similar to reality and the simulation accuracy was higher when AWFLUC was considered as a land-use change characteristic.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10050346
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 9
    Online Resource
    Online Resource
    Site Selection Improvement of Retailers Based on Spatial Competition Strategy and a Double-Channel Convolutional Neural Network
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 6 ( 2020-05-27), p. 357-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 6 ( 2020-05-27), p. 357-
    Abstract: The issue of site selection has become a critical challenge in the development of the retail industry with the growth of the Chinese economy and the improvement in the level of household consumption. Previous studies have considered the area of stores as the main factor of retail competition; however, the actual business performance of different stores in these studies was ignored. In addition, few studies have considered the differences in the spatial distribution of the factors of site selection. In this study, we discuss the improvement of site selection of small retail shops. A spatial competition index model was proposed as one of the features in estimating region market potential, and a market demand regression model of a double-channel convolutional neural network (CNN) was constructed based on the spatial correlation range of features. The study area was Guiyang, China. The experiments were based on the monthly sales data of fast-moving consumer goods retail stores in Guiyang. On the basis of the estimated results of the model, 18 sites with high potential for market demand were recommended. The performance of the proposed model was the best among well-known regression methods. Moreover, in comparison with a single-channel CNN, the proposed model decreased the root mean square error by 22.61%. Evaluation results showed that the proposed method could provide effective decision support for the issue of retail site selection.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9060357
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 10
    Online Resource
    Online Resource
    Analysis of Spatial Wharf Pattern of the Yangtze River Delta Urban Agglomeration, China
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 12 ( 2019-11-28), p. 541-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 12 ( 2019-11-28), p. 541-
    Abstract: Wharves, which play a vital role in ensuring and promoting social progress and national economic development, are important in water transportation. At present, studies on related fields mainly focus on ports. A robust research system has been formed through the continuous development of port geography from the perspective of space. However, the number of relevant studies on wharves is limited. This study explores the spatial distribution characteristics of wharves in the Yangtze River Delta Urban Agglomeration by using spatial analysis methods, such as nearest neighbor index, multi-distance spatial clustering, kernel density estimation, and standard deviation ellipse. Moreover, it evaluates the allocation level of wharves from different scales by constructing an index system based on the location data of 1264 wharves in the Yangtze River Delta Urban Agglomeration. Results show that the spatial pattern of wharves exhibits evident aggregation and regional differences. The spatial distribution of wharves is characterized by a “band” structure, which is densely distributed along the Yangtze River and the eastern coast. The allocation level of wharves presents evident agglomeration at different scales. The relationship between the spatial wharf pattern and the economy shows that high gross domestic product and total imports and exports correspond to a considerable number of wharves.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8120541
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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