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  • Cartography and geographic base data  (8)
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  • Cartography and geographic base data  (8)
  • 1
    Online Resource
    Online Resource
    A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 6 ( 2021-06-03), p. 379-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 6 ( 2021-06-03), p. 379-
    Abstract: Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can obtain specific positions, shapes, colors, etc. and people’s visual and image thinking can play a role. In addition, the theories and methods used in the space field could be applied to help people observe and analyze abstract data. However, current research has limited the use of these space theories and methods. For instance, many existing map theories and methods are not well combined. In addition, it is difficult to fully display data in virtual terrains, such as showing the structure and relationship at the same time. Facing the above problems, this study takes hierarchical data as the research object and expresses both the data structure and relationship from a spatial perspective. First, the conversion from high-dimensional non-location data to two-dimensional discrete points is achieved by a dimensionality reduction algorithm to reflect the data relationship. Based on this, kernel density estimation interpolation and fractal noise algorithms are used to construct terrain features in the virtual terrains. Under the control of the kernel density search radius and noise proportion, a multi-scale terrain model is built with the help of level of detail (LOD) technology to express the hierarchical structure and support the multi-scale analysis of data. Finally, experiments with actual data are carried out to verify the proposed method.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10060379
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 1 ( 2019-01-11), p. 23-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 1 ( 2019-01-11), p. 23-
    Abstract: Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8010023
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 11 ( 2021-10-29), p. 734-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 11 ( 2021-10-29), p. 734-
    Abstract: The city landscape is largely related to the design concept and aesthetics of planners. Influenced by globalization, planners and architects have borrowed from available designs, resulting in the “one city with a thousand faces” phenomenon. In order to create a unique urban landscape, they need to focus on local urban characteristics while learning new knowledge. Therefore, it is particularly important to explore the characteristics of cities’ landscapes. Previous researchers have studied them from different perspectives through social media data such as element types and feature maps. They only considered the content information of a image. However, social media images themselves have a “photographic cultural” character, which affects the city character. Therefore, we introduce this characteristic and propose a deep style learning for the city landscape method that can learn the global landscape features of cities from massive social media images encoded as vectors called city style features (CSFs). We find that CSFs can describe two landscape features: (1) intercity landscape features, which can quantitatively assess the similarity of intercity landscapes (we find that cities in close geographical proximity tend to have greater visual similarity to each other), and (2) intracity landscape features, which contain the inherent style characteristics of cities, and more fine-grained internal-city style characteristics can be obtained through cluster analysis. We validate the effectiveness of the above method on over four million Flickr social media images. The method proposed in this paper also provides a feasible approach for urban style analysis.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10110734
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 2 ( 2021-02-13), p. 75-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 2 ( 2021-02-13), p. 75-
    Abstract: Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10020075
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 5
    Online Resource
    Online Resource
    Geospatial Network Analysis and Origin-Destination Clustering of Bike-Sharing Activities during the COVID-19 Pandemic
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 1 ( 2023-01-13), p. 23-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 1 ( 2023-01-13), p. 23-
    Abstract: Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work–home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12010023
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 6
    Online Resource
    Online Resource
    Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 8 ( 2021-07-30), p. 515-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 8 ( 2021-07-30), p. 515-
    Abstract: Global climate change and human activities have resulted in immense changes in the Earth’s ecosystem, and the interaction between the land surface and the atmosphere is one of the most important processes. Wind is a reference for studying atmospheric dynamics and climate change, analyzing the wind speed change characteristics in historical periods, and studying the influence of wind on the Earth-atmosphere interaction; additionally, studying the wind, contributes to analyzing and alleviating a series of problems, such as the energy crisis, environmental pollution, and ecological deterioration facing human beings. In this study, data from 697 meteorological stations in China from 2000 to 2019 were used to study the distribution and trend of wind speed over the past two decades. The relationships between wind speed and climate factors were explored using statistical methods; furthermore, combined with terrain, climate change, and human activities, we quantified the contribution of environmental factors to wind speed. The results show that a downward trend was recorded before 2011, but overall, there was an increasing trend that was not significant; moreover, the wind speed changes showed obvious seasonality and were more complicated on the monthly scale. The wind speed trend mainly increased in the western region, decreased in the eastern region, was higher in the northeastern, northwestern, and coastal areas, and was lower in the central area. Temperature, bright sunshine duration, evaporation, and precipitation had a strong influence, in which wind speed showed a significant negative correlation with temperature and precipitation and vice versa for sunshine and evapotranspiration. The influence of environmental factors is diverse, and these results could help to develop environmental management strategies across ecologically fragile areas and improve the design of wind power plants to make better use of wind energy.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10080515
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 7
    Online Resource
    Online Resource
    Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 8 ( 2020-07-29), p. 475-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 8 ( 2020-07-29), p. 475-
    Abstract: The rapid growth of transportation network companies (TNCs) has reshaped the traditional taxi market in many modern cities around the world. This study aims to explore the spatiotemporal variations of built environment on traditional taxis (TTs) and TNC. Considering the heterogeneity of ridership distribution in spatial and temporal aspects, we implemented a geographically and temporally weighted regression (GTWR) model, which was improved by parallel computing technology, to efficiently evaluate the effects of local influencing factors on the monthly ridership distribution for both modes at each taxi zone. A case study was implemented in New York City (NYC) using 659 million pick-up points recorded by TT and TNC from 2015 to 2017. Fourteen influencing factors from four groups, including weather, land use, socioeconomic and transportation, are selected as independent variables. The modeling results show that the improved parallel-based GTWR model can achieve better fitting results than the ordinary least squares (OLS) model, and it is more efficient for big datasets. The coefficients of the influencing variables further indicate that TNC has become more convenient for passengers in snowy weather, while TT is more concentrated at the locations close to public transportation. Moreover, the socioeconomic properties are the most important factors that caused the difference of spatiotemporal patterns. For example, passengers with higher education/income are more inclined to select TT in the western of NYC, while vehicle ownership promotes the utility of TNC in the middle of NYC. These findings can provide scientific insights and a basis for transportation departments and companies to make rational and effective use of existing resources.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9080475
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 8
    Online Resource
    Online Resource
    Particle System-Based Multi-Hierarchy Dynamic Visualization of Ocean Current Data
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 10 ( 2021-10-01), p. 667-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 10 ( 2021-10-01), p. 667-
    Abstract: In three-dimensional (3D) digital Earth environment, there are many problems when using the existing methods to express the ocean current, such as uneven distribution of seed points, density leap in scale change and messy visualization. In this paper, a new dynamic visualization method of multi-hierarchy flow field based on particle system is proposed; Specifically, three typical spherical uniform algorithms are studied and compared, and the streamline becoming denser from the equator to the poles on globe is eliminated by placing seed points using Marsaglia polar method as the most efficient. In addition, a viewport-adaptive adjustment algorithm is proposed, which realizes that the density of particles is always suitable to any viewing distance during continuous zooming. To solve the visual representation deficiency, we design a new dynamic pattern to enhance the expression and perception of current, which makes up for the shortcoming of the arrow glyph and streamline methods. Finally, a prototype of GPU parallel and viewport coherence is achieved, whose feasibility and effectiveness are verified by a series of experiments. The results show that our method can not only represent ocean current data clearly and efficiently, but also has outstanding uniformity and hierarchy effect.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10100667
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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