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Given a grid of cells each having an associated cost value, a raster version of the least-cost path problem seeks a sequence of cells connecting two specified cells such that its total accumulated cost is minimized. Identifying least-cost paths is one of the most basic functions of raster-based geographic information systems. Existing algorithms are useful if the path width is assumed to be zero or negligible compared to the cell size. This assumption, however, may not be valid in many real-world applications ranging from wildlife corridor planning to highway alignment. This paper presents a method to solve a raster-based least-cost path problem whose solution is a path having a specified width in terms of Euclidean distance (rather than by number of cells). Assuming that all cell values are positive, it does so by transforming the given grid into a graph such that each node represents a neighborhood of a certain form determined by the specified path width, and each arc represents a possible transition from one neighborhood to another. An existing shortest path algorithm is then applied to the graph. This method is highly efficient, as the number of nodes in the transformed graph is not more than the number of cells in the given grid and decreases with the specified path width. However, a shortcoming of this method is the possibility of generating a self-intersecting path which occurs only when the given grid has an extremely skewed distribution of cost values. [ABSTRACT FROM PUBLISHER]

The neighborhood definition, which determines the influence on a cell from its nearby cells within a localized region, plays a critical role in the performance of a cellular automaton (CA) model. Raster CA models use a cellular grid to represent geographic space, and are sensitive to the cell size and neighborhood configuration. However, the sensitivity of vector-based CAs, an alternative to the raster-based counterpart, to neighborhood type and size remains uninvestigated. The present article reports the results of a detailed sensitivity analysis of an irregular CA model of urban land use dynamics. The model uses parcel data at the cadastral scale to represent geographic space, and was implemented to simulate urban growth in Central Texas, USA. Thirty neighborhood configurations defined by types and sizes were considered in order to examine the variability in the model outcome. Results from accuracy assessments and landscape metrics confirmed the model’s sensitivity to neighborhood configurations. Furthermore, the centroid intercepted neighborhood with a buffer of 120 m produced the most accurate simulation result. This neighborhood produced scattered development while the centroid extent-wide neighborhood resulted in a clustered development predominantly near the city center. [ABSTRACT FROM AUTHOR]

The demand for parallel geocomputation based on raster data is constantly increasing with the increase of the volume of raster data for applications and the complexity of geocomputation processing. The difficulty of parallel programming and the poor portability of parallel programs between different parallel computing platforms greatly limit the development and application of parallel raster-based geocomputation algorithms. A strategy that hides the parallel details from the developer of raster-based geocomputation algorithms provides a promising way towards solving this problem. However, existing parallel raster-based libraries cannot solve the problem of the poor portability of parallel programs. This paper presents such a strategy to overcome the poor portability, along with a set of parallel raster-based geocomputation operators (PaRGO) designed and implemented under this strategy. The developed operators are compatible with three popular types of parallel computing platforms: graphics processing unit supported by compute unified device architecture, Beowulf cluster supported by message passing interface (MPI), and symmetrical multiprocessing cluster supported by MPI and open multiprocessing, which make the details of the parallel programming and the parallel hardware architecture transparent to users. By using PaRGO in a style similar to sequential program coding, geocomputation developers can quickly develop parallel raster-based geocomputation algorithms compatible with three popular parallel computing platforms. Practical applications in implementing two algorithms for digital terrain analysis show the effectiveness of PaRGO. [ABSTRACT FROM PUBLISHER]

Spatial index is a key component of Geographic Information Systems (GISystems). To date, an increasing number of spatial indexes have been developed to enhance the efficiency of spatial analysis and spatial query. Approximate expressions are adopted in the foundation of spatial index construction, to assist data organisation, e.g., bounding box of vector elements can be employed to build R-tree index. However, R-tree index using such bounding box expresses elements approximately, which usually results in redundancy and excessiveness due to inherent roughness of this method. This study proposes a characteristic bitmap coding method, termed the QCODE method, which generates approximate expressions of vector elements based on self-adaptive gridding. Based on the sizes of vector elements, this method selects the grid at an appropriate level in a self-adaptive manner, discretises the vector elements into grids through a rasterisation operation, as well as compresses and encodes this information as the code of a characteristic bitmap, i.e., the QCODE. The ‘bit’ data type is used in the design of QCODE to restrict the approximate expression of vector elements into finite bytes, providing more precise filtering as compared to the case in which only bounding box is used. With its distinct characteristics, the QCODE is introduced for the improvement of R-tree index. The results of experiments show that, combined with R-tree index, this method can reduce the filtering amount of vector elements as required for spatial analysis, and accelerate the execution efficiency of the entire process of GIS spatial analysis. [ABSTRACT FROM PUBLISHER]