Wind flow is one of the elements that influence the dispersion of pollutants in air pollution events. The wind flow can be observed using the Computational Fluid Dynamics (CFD) technique. Attachment ...of the building model is required to monitor the wind flow of the urban area as the building's existence manipulates the movement of the wind throughout the urban area. In the meantime, prior research used building models with no specific modelling standard incorporated into the simulation environment. However, this research employs the building standard of City Geographic Markup Language (CityGML) to model the building involved. As referred to in the earlier research, the present level of detail 3.1 (LoD3.1) model is the least detail LoD suitable to visualize the wind simulation; this model is used as the original model to be compared with the newly generated model in 3D raster type. The vector data type is the model in the specified LoD of the CityGML standard. This study investigates the amalgamation of the raster model in the simulation environment by comparing the edge length, the simulation result, and the computational time of the rasterized model with the vectorized model. A slight edge length difference of approximately 14.5 cm is shown by the raster model. However, its simulation environment yields acceptable error values for wind velocity and pressure difference of 0.089 and 0.096, respectively, and outperforms the vector environment with a 50.4% shorter computational time. The investigation concludes that the 3D rasterized model is compatible with the wind simulation environment.
The integration of heterogeneous geospatial data offers possibilities to manually and automatically derive new information, which are not available when using only a single data source. Furthermore, ...it allows for a consistent representation and the propagation of updates from one data set to the other. However, different acquisition methods, data schemata and updating cycles of the content can lead to discrepancies in geometric and thematic accuracy and correctness which hamper the combined integration. To overcome these difficulties, appropriate methods for the integration and harmonization of data from different sources and of different types are needed. In this paper we describe two generic cases including novel integration algorithms, namely the integration of two heterogeneous vector data sets, and the integration of raster and vector data. Both algorithms are linked to a federated database which allows for automatic object matching and for managing
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m relationships. We describe and illustrate our work using vector data from topography and the geosciences, as well as multi-spectral imagery.
The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on ...this large volume of data becomes essential in several data- and compute-intensive applications. Unfortunately, most of the existing methods and approaches are based on a traditional computing framework (uniprocessors) which makes them not scalable and not adequate to deal with large-scale data. In this work, we present a high-performance query for massive spatio–temporal data. The query consists of selecting fixed size raster subsequences, based on the average of their region of interest, from a spatio–temporal raster sequence satisfying a user threshold condition. In our paper, for the purpose of simplification, we consider that the region of interest is the entire raster and not only a subregion. Our aim is to speed up the execution using parallel primitives and pure CUDA. Furthermore, we propose a new method based on a sorting step to save computations and boost the speed of the query execution. The test results show that the proposed methods are faster and good performance is achieved even with large-scale rasters and data.
Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have ...been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.
Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments. This is the case for instance in the Lidar-based and ...multi-source forest inventories of Finland where the interpretation units are 16 m × 16 m grid cells. Using these cells as simulation units in forest planning would lead to very large planning problems. This difficulty could be alleviated by aggregating the grid cells into larger homogeneous segments before planning calculations. This study developed a cellular automaton (CA) for aggregating grid cells into larger calculation units, which in this study were called stands. The criteria used in stand delineation were the shape and size of the stands, and homogeneity of stand attributes within the stand. The stand attributes were: main site type (upland or peatland forest), site fertility, mean tree diameter, mean tree height and stand basal area. In the CA, each cell was joined to one of its adjacent stands for several iterations, until the cells formed a compact layout of homogeneous stands. The CA had several parameters. Due to high number possible parameter combinations, particle swarm optimization was used to find the optimal set of parameter values. Parameter optimization aimed at minimizing within-stand variation and maximizing between-stand variation in stand attributes. When the CA was optimized without any restrictions for its parameters, the resulting stand delineation consisted of small and irregular stands. A clean layout of larger and compact stands was obtained when the CA parameters were optimized with constrained parameter values and so that the layout was penalized as a function of the number of small stands (< 0.1 ha). However, there was within-stand variation in fertility class due to small-scale variation in the data. The stands delineated by the CA explained 66–87% of variation in stand basal area, mean tree height and mean diameter, and 41–92% of variation in the fertility class of the site. It was concluded that the CA developed in this study is a flexible new tool, which could be immediately used in forest planning.
Based on remote sensing and GIS techniques, land use maps in 2000, 2005 and 2010 in China’s coastal zone were produced, and structural raster data of land use were further generated to calculate land ...use intensity comprehensive index (LUICI) for analyzing land use spatial-temporal characteristics at 1 km scale. Results show that: 1) from the perspective of spatial patterns of landforms at a macro scale, there is a significant difference in land use intensity between the north and the south of China’s coastal zone. Hotspots of changes mainly concentrated in metropolitan areas, estuaries and coastal wetlands; 2) elevation is an important factor that controlling land use spatial patterns at local scale. Land use intensity is much higher within areas below the elevation of 400 m and it decreased significantly as the elevation increasing; 3) there is a significant land-ocean gradient for land use intensity, which is low in island and near-shore areas, but high in the regions that 4–30 km far away the coastline because of much intensive human activities; however, in recent decades land use intensity had been promoted significantly in low near-shore area due to extensive sea reclamations; 4) significant differences of land use intensity were also found among provincial administrative units. A rising trend of land use intensity was found in provincial-level administrative units from 2000 to 2010. To sum up, elevation, land-ocean gradient, socio-economic status and policy are all influencing factors to the spatial patterns and temporal variations of land use intensity in China’s coastal zone.
This article presents an evaluation of the ERDAS IMAGINE Spatial Model Editor from the perspective of effective cognition. Workflow models designed in Spatial Model Editor are used for the automatic ...processing of remote sensing data. The process steps are designed as a chain of operations in the workflow model. The functionalities of the Spatial Model Editor and the visual vocabulary are both important for users. The cognitive quality of the visual vocabulary increases the comprehension of workflows during creation and utilization. The visual vocabulary influences the user’s exploitation of workflow models. The complex Physics of Notations theory was applied to the visual vocabulary on ERDAS IMAGINE Spatial Model Editor. The results were supplemented and verified using the eye-tracking method. The evaluation of user gaze and the movement of the eyes above workflow models brought real insight into the user’s cognition of the model. The main findings are that ERDAS Spatial Model Editor mostly fulfils the requirements for effective cognition of visual vocabulary. Namely, the semantic transparency and dual coding of symbols are very high, according to the Physics of Notations theory. The semantic transparency and perceptual discriminability of the symbols are verified through eye-tracking. The eye-tracking results show that the curved connector lines adversely affect the velocity of reading and produce errors. The application of the Physics of Notations theory and the eye-tracking method provides a useful evaluation of graphical notation as well as recommendations for the user design of workflow models in their practice.
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