“…An all-pairs shortest-path algorithm was then used to estimate a distance matrix.Fig. 5Maps showing employment opportunities within 5km (access 5km) of for all output areas across the GB [1]. …”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…Maps showing employment opportunities within 5km (access 5km) of for all output areas across the GB [1]. …”
Section: Experimental Design Materials and Methodsmentioning
This paper provides a brief description of three new forms of key datasets relevant to urban analytics studies namely: Transport, Housing and Employment Accessibility, covering Great Britain, developed by the Urban Big Data Centre (UBDC). Full details of the research related to this paper are contained in “Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics” [1]. The transport Dataset contains public transport availability (PTA) indicators at both the stop/station and small-area levels (lower layer super output area (LSOA) and middle layer super output area (MSOA)). The employment dataset provides information on the number of people with access to employment within specific distances from each output area. The housing datasets contains quarterly house rent and sales prices aggregated at output area level (MSOA). The theoretical background for measuring the datasets at small area levels is also presented in this paper. Additionally, a variety of raw data used to produce some of the datasets (e.g. PTA) is also included to enable interested readers to reproduce them.
“…An all-pairs shortest-path algorithm was then used to estimate a distance matrix.Fig. 5Maps showing employment opportunities within 5km (access 5km) of for all output areas across the GB [1]. …”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…Maps showing employment opportunities within 5km (access 5km) of for all output areas across the GB [1]. …”
Section: Experimental Design Materials and Methodsmentioning
This paper provides a brief description of three new forms of key datasets relevant to urban analytics studies namely: Transport, Housing and Employment Accessibility, covering Great Britain, developed by the Urban Big Data Centre (UBDC). Full details of the research related to this paper are contained in “Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics” [1]. The transport Dataset contains public transport availability (PTA) indicators at both the stop/station and small-area levels (lower layer super output area (LSOA) and middle layer super output area (MSOA)). The employment dataset provides information on the number of people with access to employment within specific distances from each output area. The housing datasets contains quarterly house rent and sales prices aggregated at output area level (MSOA). The theoretical background for measuring the datasets at small area levels is also presented in this paper. Additionally, a variety of raw data used to produce some of the datasets (e.g. PTA) is also included to enable interested readers to reproduce them.
“…The length of the point A to the inflection point O is the length M of the pipeline section that needs to be retracted. By the constraint that the radius of the circle cannot be greater than the width W of the pipeline, we can get the value of M using formulation (2):…”
Section: Smoothing Of Pipeline Inflection Pointsmentioning
confidence: 99%
“…With the continuous development and advancement of geographic information systems (GIS), it has become challenging for traditional two-dimensional GIS to meet the needs of geospatial visualizations and analysis [1,2]. With the widespread application of GIS in society, the application of three-dimensional (3D) scenes in smart cities is becoming deeper and wider [3][4][5].…”
Three-dimensional (3D) pipe network modeling plays an essential part in high performance-based smart city applications. Given that massive 3D pipe networks tend to be difficult to manage and to visualize, we propose in this study a hybrid framework for high-performance modeling of a 3D pipe network, including pipe network data model and high-performance modeling. The pipe network data model is devoted to three-dimensional pipe network construction based on network topology and building information models (BIMs). According to the topological relationships of the pipe point pipelines, the pipe network is decomposed into multiple pipe segment units. The high-performance modeling of 3D pipe network contains a spatial 3D model, the instantiation, adaptive rendering, and combination parallel computing. Spatial 3D model (S3M) is proposed for spatial data transmission, exchange, and visualization of massive and multi-source 3D spatial data. The combination parallel computing framework with GPU and OpenMP was developed to reduce the processing time for pipe networks. The results of the experiments showed that the hybrid framework achieves a high efficiency and the hardware resource occupation is reduced.
“…This consists of large feature space in different structures, which might have been collected from various sources [8]. Many computational methods like Random forest, Support Vector Machine (SVM) have been developed to handle the Big data and these methods are able to handle the large datasets based on the measurements and samples [9]. Previously, integration and analysis of large volume of data is an enormous task.…”
Big data is large-scale data collected for knowledge discovery, it has been widely used in various applications. Big data often has image data from the various applications and requires effective technique to process data. In this paper, survey has been done in the big image data researches to analysis the effective performance of the methods. Deep learning techniques provides the effective performance compared to other methods included wavelet based methods. The deep learning techniques has the problem of requiring more computational time, and this can be overcome by lightweight methods.
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