Public space as an extension of private living spaces carries the different social life and customs of human settlement.To analyze the spatial distribution characteristics of traditional villages in northern Guangxi b...Public space as an extension of private living spaces carries the different social life and customs of human settlement.To analyze the spatial distribution characteristics of traditional villages in northern Guangxi based on spatial syntax and its influencing factors,this paper analyzed and compared the spatial structure and morphology of traditional villages in northern Guangxi by using the theory of spatial syntax and linguistics as the quantitative analysis method of spatial syntax,and verified the feasibility of expanding the application of spatial syntax,finally,the generality and characteristics of the spatial structure and form of traditional villages in northern Guangxi were put forward.Protection has been implemented.According to the comprehensibility data in this paper,the comprehensibility of the village 1 in northern Guangxi is 0.52,the village 2 is 0.40,the village 3 is 0.35,the village 4 is 0.48,the village 5 is 0.55 and the village 6 is 0.50.It showed that in the selected 6 village samples,except for the 3 ones in northern Guangxi,the local space of the other 3 villages could better perceive the overall space,which also reflected the overall space permeability of most traditional villages in northern Guangxi was good.展开更多
In the research field of spatiotemporal data discovery,how to utilize the semantic characteristics of spatiotemporal datasets is an important topic.This paper presented a content-based recommendation method,and applie...In the research field of spatiotemporal data discovery,how to utilize the semantic characteristics of spatiotemporal datasets is an important topic.This paper presented a content-based recommendation method,and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery.The source data of this research was from the MUDROD(Mining and Utilizing Dataset Relevancy from Oceanographic Datasets)search platform.From the historical search log,major keywords were extracted and organized according to ontologies in a hierarchical structure.Using the search history,the posterior probability between each subclass and their super class in the ontologies was calculated,indicating a recommendation likelihood.We created a Bayesian network model for inference based on ontologies.This model can address the following two objectives:(1)Given one class in the ontology,the model can judge which class has the biggest likelihood to be selected for recommendation.(2)Based on the search history of a user,the Bayesian network model can judge which class has the biggest probability to be recommended.Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery.展开更多
Urban sensing is one of the fundamental building blocks of urban computing.It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural...Urban sensing is one of the fundamental building blocks of urban computing.It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas.Nevertheless,issues such as uneven distribution,low sampling rate and high failure ratio of sensors often make their readings less reliable.This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them inclividually.This can be achieved by connecting data through monitored objects,using the Skip-gram model to estimate spatial correlation and long shortterm memory to estimate temporal correlation.The framework consists of three major modules:1)a space embedded Bidirectional Long Short-Term Memory(BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data;2)a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data;3)an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data.The approach is evaluated with real-world data collected by over 3000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China,and the result is superior to those of several referenced approaches.With a 12.9%improvement,in data accuracy over the raw data,the proposed framework plays a significant,role in various real-world use cases in urban governance,such as criminal investigation,traffic violation monitoring,and equipment maintenance.展开更多
基金Sponsored by the Project of Enhancing Basic Scientific Research Ability of Young and Middle-aged Teachers in Guangxi Universities in 2021:Research on the Distribution Characteristics and Architectural Style of Minority Settlements in Typical Areas of Northern Guangxi (2021KY0166)the Scientific Research Foundation of Guangxi University for Nationalities in 2020:Study on the Characteristics of Slope Sliding Surface and Early Warning of Landslide (2020KJQD26)。
文摘Public space as an extension of private living spaces carries the different social life and customs of human settlement.To analyze the spatial distribution characteristics of traditional villages in northern Guangxi based on spatial syntax and its influencing factors,this paper analyzed and compared the spatial structure and morphology of traditional villages in northern Guangxi by using the theory of spatial syntax and linguistics as the quantitative analysis method of spatial syntax,and verified the feasibility of expanding the application of spatial syntax,finally,the generality and characteristics of the spatial structure and form of traditional villages in northern Guangxi were put forward.Protection has been implemented.According to the comprehensibility data in this paper,the comprehensibility of the village 1 in northern Guangxi is 0.52,the village 2 is 0.40,the village 3 is 0.35,the village 4 is 0.48,the village 5 is 0.55 and the village 6 is 0.50.It showed that in the selected 6 village samples,except for the 3 ones in northern Guangxi,the local space of the other 3 villages could better perceive the overall space,which also reflected the overall space permeability of most traditional villages in northern Guangxi was good.
基金Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund.
文摘In the research field of spatiotemporal data discovery,how to utilize the semantic characteristics of spatiotemporal datasets is an important topic.This paper presented a content-based recommendation method,and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery.The source data of this research was from the MUDROD(Mining and Utilizing Dataset Relevancy from Oceanographic Datasets)search platform.From the historical search log,major keywords were extracted and organized according to ontologies in a hierarchical structure.Using the search history,the posterior probability between each subclass and their super class in the ontologies was calculated,indicating a recommendation likelihood.We created a Bayesian network model for inference based on ontologies.This model can address the following two objectives:(1)Given one class in the ontology,the model can judge which class has the biggest likelihood to be selected for recommendation.(2)Based on the search history of a user,the Bayesian network model can judge which class has the biggest probability to be recommended.Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery.
基金supported in part by the National Key Research and Development Program of China under Grant No.2018YFC0-831500.
文摘Urban sensing is one of the fundamental building blocks of urban computing.It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas.Nevertheless,issues such as uneven distribution,low sampling rate and high failure ratio of sensors often make their readings less reliable.This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them inclividually.This can be achieved by connecting data through monitored objects,using the Skip-gram model to estimate spatial correlation and long shortterm memory to estimate temporal correlation.The framework consists of three major modules:1)a space embedded Bidirectional Long Short-Term Memory(BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data;2)a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data;3)an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data.The approach is evaluated with real-world data collected by over 3000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China,and the result is superior to those of several referenced approaches.With a 12.9%improvement,in data accuracy over the raw data,the proposed framework plays a significant,role in various real-world use cases in urban governance,such as criminal investigation,traffic violation monitoring,and equipment maintenance.
