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Yong Gao
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China

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Journal article
Published: 16 June 2021 in ISPRS International Journal of Geo-Information
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Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin–destination (O–D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural–urban gap in medical school applications, and reduce physician shortages in rural areas.

ACS Style

Lan Mu; Yusi Liu; Donglan Zhang; Yong Gao; Michelle Nuss; Janani Rajbhandari-Thapa; Zhuo Chen; José Pagán; Yan Li; Gang Li; Heejung Son. Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States. ISPRS International Journal of Geo-Information 2021, 10, 417 .

AMA Style

Lan Mu, Yusi Liu, Donglan Zhang, Yong Gao, Michelle Nuss, Janani Rajbhandari-Thapa, Zhuo Chen, José Pagán, Yan Li, Gang Li, Heejung Son. Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States. ISPRS International Journal of Geo-Information. 2021; 10 (6):417.

Chicago/Turabian Style

Lan Mu; Yusi Liu; Donglan Zhang; Yong Gao; Michelle Nuss; Janani Rajbhandari-Thapa; Zhuo Chen; José Pagán; Yan Li; Gang Li; Heejung Son. 2021. "Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States." ISPRS International Journal of Geo-Information 10, no. 6: 417.

Research article
Published: 02 March 2021 in Transactions in GIS
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In the context of rapid development, Beijing, the capital of China, is facing huge challenges in providing fair healthcare resources to residents. Although Beijing has the best healthcare resources nationwide, a highly concentrated population and uneven distribution of hospitals make the supply of medical resources tight and unbalanced. The objective of this study is to explore the healthcare resource inequality in Beijing based on spatial accessibility. The two‐step floating catchment area method was improved to measure healthcare accessibility by defining a novel distance attenuation function that conforms to the specific travel behavior of taxies to hospitals. We explored the inequality among different places and different populations. It was found that the spatial inequality of healthcare resources was evident and typical, with the dominant resources concentrated in the city center. Some regions are always in an advantageous position regardless of traffic conditions. The impact of some social‐economic factors on healthcare accessibility was analyzed, which exhibited significant spatial heterogeneity. Hospital deserts for different vulnerable populations were identified. Besides children with massive hospital deserts at the city fringe, other vulnerable populations have no distinct disadvantage. These results offer profound comprehension of healthcare inequality to assist in healthcare resources management and policy‐making in Beijing.

ACS Style

Shize Gong; Yong Gao; Fan Zhang; Lan Mu; Chaogui Kang; Yu Liu. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Transactions in GIS 2021, 25, 1504 -1521.

AMA Style

Shize Gong, Yong Gao, Fan Zhang, Lan Mu, Chaogui Kang, Yu Liu. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Transactions in GIS. 2021; 25 (3):1504-1521.

Chicago/Turabian Style

Shize Gong; Yong Gao; Fan Zhang; Lan Mu; Chaogui Kang; Yu Liu. 2021. "Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement." Transactions in GIS 25, no. 3: 1504-1521.

Research article
Published: 15 July 2020 in Environment and Planning B: Urban Analytics and City Science
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Recognizing urban functions is crucial for understanding urban spatial structures and urban planning. Previous work has investigated urban functions based on human activities that were derived from mobile phone positioning data, check-in data, taxi data, etc. However, urban functions can only be comprehensively sensed from both human activities and the physical environment together. To do so, a deep learning method was proposed to predict urban functions by integrating social media data and street-level imagery. The verbs extracted from social media posts were taken as the proxy for human activities, and we identified urban physical environmental information from street-level imagery. Then urban functions were uncovered from both the verbs in terms of human activities and street-level imagery from the perspective of the physical environment. Twelve types of urban function were recognized by verbs in social media posts, which were then improved by integrating street-level imagery within the 5th Ring Road of Beijing, China. The experiment demonstrated that verbs as direct proxies for human activities can avoid noise, and the multi-source data integration eliminated biases caused by a single data source. This work provides a comprehensive understanding of urban structure and dynamics for urban management and planning.

ACS Style

Chao Ye; Fan Zhang; Lan Mu; Yong Gao; Yu Liu. Urban function recognition by integrating social media and street-level imagery. Environment and Planning B: Urban Analytics and City Science 2020, 1 .

AMA Style

Chao Ye, Fan Zhang, Lan Mu, Yong Gao, Yu Liu. Urban function recognition by integrating social media and street-level imagery. Environment and Planning B: Urban Analytics and City Science. 2020; ():1.

Chicago/Turabian Style

Chao Ye; Fan Zhang; Lan Mu; Yong Gao; Yu Liu. 2020. "Urban function recognition by integrating social media and street-level imagery." Environment and Planning B: Urban Analytics and City Science , no. : 1.

Journal article
Published: 01 July 2020 in IEEE Transactions on Intelligent Transportation Systems
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Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility patterns. However, existing models or approaches neglect the network structure of spatial flows, thus resulting in inappropriate estimates and a low performance. The development of graph neural networks offers a powerful tool to deal with graph-structured data. In this article, we proposed a spatial interaction graph convolutional network model, which combines graph convolution and a mapping function to predict flow data from the perspective of network learning. This model utilizes geographical unit embedding in local spatial networks to improve prediction accuracy. A negative sampling technique is adopted to reduce misestimation. Experiments on Beijing taxi trip data verified the usefulness of our model in spatial flow prediction. We also demonstrated that a biased training sample had a negative impact on the model's performance. More attributes of geographical units, a more proper negative sampling rate and a larger training set can increase the prediction accuracy of flow data.

ACS Style

Xin Yao; Yong Gao; Di Zhu; Ed Manley; Jiaoe Wang; Yu Liu. Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks. IEEE Transactions on Intelligent Transportation Systems 2020, 1 -11.

AMA Style

Xin Yao, Yong Gao, Di Zhu, Ed Manley, Jiaoe Wang, Yu Liu. Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks. IEEE Transactions on Intelligent Transportation Systems. 2020; (99):1-11.

Chicago/Turabian Style

Xin Yao; Yong Gao; Di Zhu; Ed Manley; Jiaoe Wang; Yu Liu. 2020. "Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks." IEEE Transactions on Intelligent Transportation Systems , no. 99: 1-11.

Research article
Published: 14 November 2019 in PLOS ONE
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The scale effect is an important research topic in the field of geography. When aggregating individual-level data into areal units, encountering the scale problem is inevitable. This problem is more substantial when mining collective patterns from big geo-data due to the characteristics of extensive spatial data. Although multi-scale models were constructed to mitigate this issue, most studies still arbitrarily choose a single scale to extract spatial patterns. In this research, we introduce the nugget-sill ratio (NSR) derived from semi-variograms as an indicator to extract the optimal scale. We conducted two simulated experiments to demonstrate the feasibility of this method. Our results showed that the optimal scale is negatively correlated with spatial point density, but positively correlated with the degree of dispersion in a point pattern. We also applied the proposed method to a case study using Weibo check-in data from Beijing, Shanghai, Chengdu, and Wuhan. Our study provides a new perspective to measure the spatial heterogeneity of big geo-data and selects an optimal spatial scale for big data analytics.

ACS Style

Lei Chen; Yong Gao; Di Zhu; Yihong Yuan; Yu Liu. Quantifying the scale effect in geospatial big data using semi-variograms. PLOS ONE 2019, 14, e0225139 .

AMA Style

Lei Chen, Yong Gao, Di Zhu, Yihong Yuan, Yu Liu. Quantifying the scale effect in geospatial big data using semi-variograms. PLOS ONE. 2019; 14 (11):e0225139.

Chicago/Turabian Style

Lei Chen; Yong Gao; Di Zhu; Yihong Yuan; Yu Liu. 2019. "Quantifying the scale effect in geospatial big data using semi-variograms." PLOS ONE 14, no. 11: e0225139.

Journal article
Published: 04 August 2019 in Sustainability
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Taxi services provide an urban transport option to citizens. Massive taxi trajectories contain rich information for understanding human travel activities, which are essential to sustainable urban mobility and transportation. The origin and destination (O-D) pairs of urban taxi trips can reveal the spatiotemporal patterns of human mobility and then offer fundamental information to interpret and reform formal, functional, and perceptual regions of cities. Matrices are one of the most effective models to represent taxi trajectories and O-D trips. Among matrix representations, non-negative matrix factorization (NMF) gives meaningful interpretations of complex latent relationships. However, the independence assumption for observations is violated by spatial and temporal autocorrelation in taxi flows, which is not compensated in classical NMF models. In order to discover human intra-urban mobility patterns, a novel spatiotemporal constraint NMF (STC-NMF) model that explicitly solves spatial and temporal dependencies is proposed in this paper. It factorizes taxi flow matrices in both spatial and temporal aspects, thus revealing inherent spatiotemporal patterns. With three-month taxi trajectories harvested in Beijing, China, the STC-NMF model is employed to investigate taxi travel patterns and their spatial interaction modes. As the results, four departure patterns, three arrival patterns, and eight spatial interaction patterns during weekdays and weekends are discovered. Moreover, it is found that intensive movements within certain time windows are significantly related to region functionalities and the spatial interaction flows exhibit an obvious distance decay tendency. The outcome of the proposed model is more consistent with the inherent spatiotemporal characteristics of human intra-urban movements. The knowledge gained in this research would be useful to taxi services and transportation management for promoting sustainable urban development.

ACS Style

Yong Gao; Jiajun Liu; Yan Xu; Lan Mu; Yu Liu. A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips. Sustainability 2019, 11, 4214 .

AMA Style

Yong Gao, Jiajun Liu, Yan Xu, Lan Mu, Yu Liu. A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips. Sustainability. 2019; 11 (15):4214.

Chicago/Turabian Style

Yong Gao; Jiajun Liu; Yan Xu; Lan Mu; Yu Liu. 2019. "A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips." Sustainability 11, no. 15: 4214.

Journal article
Published: 03 July 2019 in Geo-spatial Information Science
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ACS Style

Yong Gao; Jing Cheng; Haohan Meng; Yu Liu. Measuring spatio-temporal autocorrelation in time series data of collective human mobility. Geo-spatial Information Science 2019, 22, 166 -173.

AMA Style

Yong Gao, Jing Cheng, Haohan Meng, Yu Liu. Measuring spatio-temporal autocorrelation in time series data of collective human mobility. Geo-spatial Information Science. 2019; 22 (3):166-173.

Chicago/Turabian Style

Yong Gao; Jing Cheng; Haohan Meng; Yu Liu. 2019. "Measuring spatio-temporal autocorrelation in time series data of collective human mobility." Geo-spatial Information Science 22, no. 3: 166-173.

Journal article
Published: 27 June 2019 in Sustainability
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Spatial patterns of tourist mobility are important for tourism management and planning. A large number of traveler-generated content accumulated on the internet provide a unique opportunity for revealing comprehensive spatial patterns of tourist movements. Instead of concentrating on a single city or attraction in previous research, this work investigates the intercity travel flows extracted from the online travel blogs in China from 2012 to 2016. The descriptive statistics of travel flows are first analyzed. The distribution of travel volume is found to satisfy the power-law distribution. Based on the intercity travel flows, a network structure is then constructed to investigate tourism interactions between cities. After four communities and 14 sub-communities being detected from the network, a tourism spatial layout with regional agglomeration effects are recognized. This research concludes that distance is essential in determining tourist movements based on a spatial interaction model. Intercity travel flows decline with distance under a power-law function. These results reveal the spatial patterns of tourist movements at an intercity scale. It will be helpful for arranging tourism resources, predicting tourist flows, and maintaining sustainable tourism.

ACS Style

Yong Gao; Chao Ye; Xiang Zhong; Lun Wu; Yu Liu. Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs. Sustainability 2019, 11, 3526 .

AMA Style

Yong Gao, Chao Ye, Xiang Zhong, Lun Wu, Yu Liu. Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs. Sustainability. 2019; 11 (13):3526.

Chicago/Turabian Style

Yong Gao; Chao Ye; Xiang Zhong; Lun Wu; Yu Liu. 2019. "Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs." Sustainability 11, no. 13: 3526.

Journal article
Published: 10 August 2018 in IEEE Access
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Massive flows that represent the individual level of movements and communications can be easily obtained in the age of big data. Generalizing spatial and temporal flow patterns from such data is essential to demonstrate spatial connections and mobility trends. Clustering approaches provide effective methods to handle data sets that contain massive individual-level flows. However, existing flow clustering studies obscure the geometric properties of flow data, such as direction and length, which significantly indicate movement trends. In addition, temporal information is often ignored because previous approaches have mainly focused on the perspective of spatial clusters of flow data, resulting in a loss of temporal patterns. In this paper, we introduce new spatial and temporal similarity measurements between flows and propose a new clustering approach of flow data based on a stepwise strategy. This method can identify clusters from distinct flow distributions and discover significant spatio-temporal trends from large flow data. Simulated experiments with synthetic flows and a case study using Beijing taxi trip data are conducted to validate the usefulness of the proposed method.

ACS Style

Xin Yao; Di Zhu; Yong Gao; Lun Wu; Pengcheng Zhang; Yu Liu. A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends. IEEE Access 2018, 6, 44666 -44675.

AMA Style

Xin Yao, Di Zhu, Yong Gao, Lun Wu, Pengcheng Zhang, Yu Liu. A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends. IEEE Access. 2018; 6 ():44666-44675.

Chicago/Turabian Style

Xin Yao; Di Zhu; Yong Gao; Lun Wu; Pengcheng Zhang; Yu Liu. 2018. "A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends." IEEE Access 6, no. : 44666-44675.

Research article
Published: 03 April 2018 in International Journal of Geographical Information Science
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The efficiency of taxi services in big cities influences not only the convenience of peoples’ travel but also urban traffic and profits for taxi drivers. To balance the demands and supplies of taxicabs, spatio-temporal knowledge mined from historical trajectories is recommended for both passengers finding an available taxicab and cabdrivers estimating the location of the next passenger. However, taxi trajectories are long sequences where single-step optimization cannot guarantee the global optimum. Taking long-term revenue as the goal, a novel method is proposed based on reinforcement learning to optimize taxi driving strategies for global profit maximization. This optimization problem is formulated as a Markov decision process for the whole taxi driving sequence. The state set in this model is defined as the taxi location and operation status. The action set includes the operation choices of empty driving, carrying passengers or waiting, and the subsequent driving behaviors. The reward, as the objective function for evaluating driving policies, is defined as the effective driving ratio that measures the total profit of a cabdriver in a working day. The optimal choice for cabdrivers at any location is learned by the Q-learning algorithm with maximum cumulative rewards. Utilizing historical trajectory data in Beijing, the experiments were conducted to test the accuracy and efficiency of the method. The results show that the method improves profits and efficiency for cabdrivers and increases the opportunities for passengers to find taxis as well. By replacing the reward function with other criteria, the method can also be used to discover and investigate novel spatial patterns. This new model is prior knowledge-free and globally optimal, which has advantages over previous methods.

ACS Style

Yong Gao; Dan Jiang; Yan Xu. Optimize taxi driving strategies based on reinforcement learning. International Journal of Geographical Information Science 2018, 32, 1677 -1696.

AMA Style

Yong Gao, Dan Jiang, Yan Xu. Optimize taxi driving strategies based on reinforcement learning. International Journal of Geographical Information Science. 2018; 32 (8):1677-1696.

Chicago/Turabian Style

Yong Gao; Dan Jiang; Yan Xu. 2018. "Optimize taxi driving strategies based on reinforcement learning." International Journal of Geographical Information Science 32, no. 8: 1677-1696.

Journal article
Published: 15 July 2016 in ISPRS International Journal of Geo-Information
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In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has become the prominent issue, needing to be resolved in high priority. The key technologies in geographic information retrieval are modeling document footprints and ranking documents based on their similarity evaluation. The traditional spatial similarity evaluation methods are mainly performed using a MBR (Minimum Bounding Rectangle) footprint model. However, due to its nature of simplification and roughness, the results of traditional methods tend to be isotropic and space-redundant. In this paper, a new model that constructs the footprints in the form of point-sets is presented. The point-set-based footprint coincides the nature of place names in web pages, so it is redundancy-free, consistent, accurate, and anisotropic to describe the spatial extents of documents, and can handle multi-scale geographic information. The corresponding spatial ranking method is also presented based on the point-set-based model. The new similarity evaluation algorithm of this method firstly measures multiple distances for the spatial proximity across different scales, and then combines the frequency of place names to improve the accuracy and precision. The experimental results show that the proposed method outperforms the traditional methods with higher accuracies under different searching scenarios.

ACS Style

Yong Gao; Dan Jiang; Xiang Zhong; Jingyi Yu. A Point-Set-Based Footprint Model and Spatial Ranking Method for Geographic Information Retrieval. ISPRS International Journal of Geo-Information 2016, 5, 122 .

AMA Style

Yong Gao, Dan Jiang, Xiang Zhong, Jingyi Yu. A Point-Set-Based Footprint Model and Spatial Ranking Method for Geographic Information Retrieval. ISPRS International Journal of Geo-Information. 2016; 5 (7):122.

Chicago/Turabian Style

Yong Gao; Dan Jiang; Xiang Zhong; Jingyi Yu. 2016. "A Point-Set-Based Footprint Model and Spatial Ranking Method for Geographic Information Retrieval." ISPRS International Journal of Geo-Information 5, no. 7: 122.

Research article
Published: 01 January 2013 in Environment and Planning B: Planning and Design
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In this study we estimate urban traffic flow using GPS-enabled taxi trajectory data in Qingdao, China, and examine the capability of the betweenness centrality of the street network to predict traffic flow. The results show that betweenness centrality is not a good predictor variable for urban traffic flow, which has, theoretically, been pointed out in existing literature. With a critique of the betweenness centrality as a predictor, we further analyze the characteristics of betweenness centrality and point out the ‘gap’ between this centrality measure and actual flow. Rather than considering only the topological properties of a street network, we take into account two aspects, the spatial heterogeneity of human activities and the distance-decay law, to explain the observed traffic-flow distribution. The spatial distribution of human activities is estimated using mobile phone Erlang values, and the power law distance decay is adopted. We run Monte Carlo simulations to generate trips and predict traffic-flow distributions, and use a weighted correlation coefficient to measure the goodness of fit between the observed and the simulated data. The correlation coefficient achieves the maximum (0.623) when the exponent equals 2.0, indicating that the proposed model, which incorporates geographical constraints and human mobility patterns, can interpret urban traffic flow well.

ACS Style

Song Gao; Yaoli Wang; Yong Gao; Yu Liu. Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality. Environment and Planning B: Planning and Design 2013, 40, 135 -153.

AMA Style

Song Gao, Yaoli Wang, Yong Gao, Yu Liu. Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality. Environment and Planning B: Planning and Design. 2013; 40 (1):135-153.

Chicago/Turabian Style

Song Gao; Yaoli Wang; Yong Gao; Yu Liu. 2013. "Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality." Environment and Planning B: Planning and Design 40, no. 1: 135-153.

Article
Published: 01 May 2010 in Science China Technological Sciences
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Digital Earth has been a hot topic and research trend since it was proposed, and Digital China has drawn much attention in China. As a key technique to implement Digital China, grid is an excellent and promising concept to construct a dynamic, inter-domain and distributed computing environment. It is appropriate to process geographic information across dispersed computing resources in networks effectively and cooperatively. A distributed spatial computing prototype system is designed and implemented with the Globus Toolkit. Several important aspects are discussed in detail. The architecture is proposed according to the characteristics of grid firstly, and then the spatial resource query and access interfaces are designed for heterogeneous data sources. An open-up hierarchical architecture for resource discovery and management is represented to detect spatial and computing resources in grid. A standard spatial job management mechanism is implemented by grid service for convenient use. In addition, the control mechanism of spatial datasets access is developed based on GSI. The prototype system utilizes the Globus Toolkit to implement a common distributed spatial computing framework, and it reveals the spatial computing ability of grid to support Digital China.

ACS Style

Lun Wu; Menglong Yan; Yong Gao; Zhenzhen Yang; Yong Zhao; Bin Chen. A distributed spatial computing prototype system in grid environment. Science China Technological Sciences 2010, 53, 25 -32.

AMA Style

Lun Wu, Menglong Yan, Yong Gao, Zhenzhen Yang, Yong Zhao, Bin Chen. A distributed spatial computing prototype system in grid environment. Science China Technological Sciences. 2010; 53 (1):25-32.

Chicago/Turabian Style

Lun Wu; Menglong Yan; Yong Gao; Zhenzhen Yang; Yong Zhao; Bin Chen. 2010. "A distributed spatial computing prototype system in grid environment." Science China Technological Sciences 53, no. 1: 25-32.

Article
Published: 01 April 2008 in Science China Technological Sciences
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Directed lines are fundamental geometric elements to represent directed linear entities. The representations of their topological relations are so different from those of simple lines that they cannot be solved exactly with normal methods. In this paper, a new model based on point-set topology is defined to represent the topological relations between directed lines and simple geometries. Through the intersections between the start-points, end-points, and interiors of the directed lines and the interiors, boundaries, and exteriors of the simple geometries, this model identifies 5 cases of topological relations between directed lines and points, 39 cases of simple lines, and 26 cases of simple polygons. Another 4 cases of simple lines and one case of simple polygons are distinguished if considering the exteriors of the directed lines. All possible cases are furthermore grouped into an exclusive and complete set containing 11 named predicts. And the conceptual neighborhood graph is set up to illustrate their relationship and similarity. This model can provide a basis for natural language description and spatial query language to present the dynamic semantics of directed lines relative to the background features.

ACS Style

Yong Gao; Yi Zhang; Yuan Tian; Jingnong Weng. Topological relations between directed lines and simple geometries. Science China Technological Sciences 2008, 51, 91 -101.

AMA Style

Yong Gao, Yi Zhang, Yuan Tian, Jingnong Weng. Topological relations between directed lines and simple geometries. Science China Technological Sciences. 2008; 51 (1):91-101.

Chicago/Turabian Style

Yong Gao; Yi Zhang; Yuan Tian; Jingnong Weng. 2008. "Topological relations between directed lines and simple geometries." Science China Technological Sciences 51, no. 1: 91-101.

Book chapter
Published: 05 July 2007 in Advances in Intelligent and Soft Computing
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Rough model is proposed to model vague geographic objects based on the definition of rough regions, lower approximation regions, upper approximation regions and boundary regions. With the combination of rough model and RCC-D-8, which is a discrete version of RCC extended into two-dimensional discrete space (ℤ2), 599 topological relations are got between simple vague geographic objects in raster regions described by intersection and containing matrix.

ACS Style

Zhenji Gao; Lun Wu; Yong Gao. Topological Relations Between Vague Objects in Discrete Space Based on Rough Model. Advances in Intelligent and Soft Computing 2007, 40, 852 -861.

AMA Style

Zhenji Gao, Lun Wu, Yong Gao. Topological Relations Between Vague Objects in Discrete Space Based on Rough Model. Advances in Intelligent and Soft Computing. 2007; 40 ():852-861.

Chicago/Turabian Style

Zhenji Gao; Lun Wu; Yong Gao. 2007. "Topological Relations Between Vague Objects in Discrete Space Based on Rough Model." Advances in Intelligent and Soft Computing 40, no. : 852-861.