The spatial locality and a spatial indexing method by dynamic clustering in hypermap system

Adaptive indexing allows for the progressive and simultaneous query-driven exploration and indexing of memoryresident data, starting as soon as they become available without upfront indexing. This technique has been so far applied to onedimensional and multidimensional data, as well as to objects with spatial extent arising in geographic information systems. However, existing spatial adaptive indexing methods cater to static data made available in an one-off manner. To date, no spatial adaptive indexing method can ingest data updates interleaved with data exploration. In this paper we introduce GLIDE, a novel method that intertwines the adaptive indexing and incremental updating of a spatial-object data set. GLIDE builds a hierarchical spatial index incrementally in response to queries and also ingests updates judiciously into it. We examine several design choices and settle for a variant that combines gradual self-driven top-down insertions with query-driven indexing operations. In an extensive experimental comparison, we show that GLIDE achieves a lower cumulative cost than upfront-indexing methods and adaptiveindexing baselines.

In the emerging era of information and communication technologies, geotechnology is one of the fastest growing fields. Geo-RDBMS is very important and evolving aspect for GIS, as it can manage large volume of spatial data inside RDBMS. The utilization of RDBMS for geospatial data was one of the important focuses of GIS professionals in last decades to store and manage 2D geo-data. However, the support for 3D geo-data inside RDBMS is still limited and is a challenging task for RDBMS providers. In this study, data organization and performance assessment of 3D geo-data inside RDBMS are carried out. In this process, various file-based 3D data models such as CityGML, COLLADA and KML are migrated to geo-RDBMS to bring entire 3D geo-data in common platform. Various spatial indexing techniques viz. R-Tree, B-Tree, GiST, etc. are applied on these 3D data models and best indexing techniques are studied for 3D GIS operations.

A spatial frequency index method is proposed to cull occlusion and generate a hologram. Object points with the same spatial frequency are put into a set for their mutual occlusion. The hidden surfaces of the three-dimensional (3D) scene are quickly removed through culling the object points that are furthest from the hologram plane in the set. The phases of plane wave, which are only interrelated with the spatial frequencies, are precomputed and stored in a table. According to the spatial frequency of the object points, the phases of plane wave for generating fringes are obtained directly from the table. Three 3D scenes are chosen to verify the spatial frequency index method. Both numerical simulation and optical reconstruction are performed. Experimental results demonstrate that the proposed method can cull the hidden surfaces of the 3D scene correctly. The occlusion effect of the 3D scene can be well reproduced. The computational speed is better than that obtained using conventional methods but is still time-consuming.

Polygon intersection is important for data processing in geographic information systems. For large datasets, spatial indexing methods such as R-tree allow the identification of polygon intersections, but often retrieve inaccurate results. An improved boundary algebra filling (iBAF) method was preliminarily proposed as an alternative to R-tree. However, its applicability, performance, and accuracy require optimization, and its application conditions remain to be unveiled. This study develops version iBAF 2.0 for a more efficient identification and evaluates performance for different computational intensities and applications. Both intersecting polygons and raster zones within intersections can be rapidly grouped in the rasterized cells of input polygons. The resulting polygons can then be generated by configuring the polygon groups or converting the zones into vectors. We use complexity ratio CR, which is defined as the sum of the number of polygons in each actually intersecting group divided by the total number of polygons, to represent the computational intensity. Two land-use datasets containing 4295 and 741,562 polygons are considered, and we establish test cases containing the same polygons with varying CR. Experimental results show that iBAF 2.0 outperforms R-tree when applied to topology verification; however, its performance is conditional for polygon overlay and area calculation between two layers. Specifically, iBAF 2.0 exhibits higher-efficiency grouping of polygons and raster zones when CR exceeds specific thresholds. In addition, better scalability is achieved compared to R-tree when polygons with complex shapes and additional layers are considered.

In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings.

Spatial indexing technology is widely used in Geographic Information Systems (GIS) and spatial databases. As a data retrieval technology, spatial indexing is becoming increasingly important in the big-data age. The purpose of this study is to propose a unified indexing strategy for the mixed data of a future marine GIS. First, data organisation of the system is described. Second, the display condition of each type of data is introduced. These conditions are the basis for the construction of a unified indexing structure. Third, a unified indexing structure for mixed data is presented. The construction process and the search method of the indexing structure are described. Finally, we implement the indexing strategy in our system “Automotive Intelligent Chart Three-dimensional Electronic Chart Display and Information Systems” (AIC 3D ECDIS). Our strategy can provide fast and integrated data retrieval. The spatial indexing strategy we propose breaks through the limitation of data types in our system. It can also be applied in other GIS systems. With the advent of the big-data age, mixed data indexing will become more and more important.

Relative space-based GIS data model to analyze the group dynamics of moving objects

On multi-scale display of geometric objects

The article, prepared as part of a dissertation research on developing a methodology for 3D spatial modelling and visualization of a specific type of natural and anthropogenic environmental objects using geographic information systems (GIS), examines and characterizes the identification features of these modelling objects. The advancement of computer technologies naturally leads to the improvement of modelling tools and methods. A special role is played by the creation of cartographic models through GIS. It has been established that certain natural and anthropogenic objects cannot be adequately modelled or visualized using standard methods due to the unavailability of their shape and dimensions for direct measurement. Their preliminary identification and separation from the surrounding environment is a crucial task for enhancing spatial modelling and mapping technologies that utilize GIS. To identify these objects, the author proposes a system of three criteria, including the criterion of spatial localization, the criterion of implicit localization and the criterion of spatial configuration. A key feature is parametric (essential) demarcation — a method for determining object boundaries based on measurements of key parameters in accordance with research classifications. Examples of objects classified as this specific type include: airborne impurity clouds, placer deposits of valuable minerals (or useful components), soil and ground contamination bodies. The conducted research aims to develop GIS tools and improve the accuracy of modelling spatially distributed objects that are difficult to identify.

The Naval Oceanographic Office has constructed a high-resolution geophysical database (GDB) for the Western Barents Sea describing the properties of the seafloor and subsurface sediment layers important to acoustic modeling. The original implementation of this database used a public domain geographic information system (GIS) called GRASS (Geographic Resource Analysis Support System). Subsequent versions use a commercial GIS, ARC/INFO. While GRASS and subsequently ARC/INFO address the needs of the Naval Oceanographic Office as well as the R&D community currently using GDB, its large data files, GIS system size, and database access time, limit the use of this database on Navy operational systems, where access time and disk space are limited. Additionally, the data stored in GDB are not immediately compatible with the Navy's standard propagation loss models. This paper discusses efforts made toward porting the Western Barents Sea GDB onto an operational system which include regionalizing the data to accommodate severe disk space limitations, new storage and retrieval methods, and on-the-fly conversion of GDB data to data types compatible with Navy standard models. The result is a full implementation of the Barents Sea GDB on a PC-based model operating system, without sacrificing many appealing features of the GIS.

Implementing spatial segregation measures in GIS

Sequences constitute a large portion of data stored in databases. Data mining applications require the ability to process similarity queries over a large amount of time series data. The query processing performance is an important factor that needs to be taken into consideration. This article proposes a similarity retrieval algorithm for time series. The proposed approach utilizes wavelet transformation in order to reduce the dimensionality of the time series. The transformed series are indexed using X-Trees, which is a spatial indexing technique able to efficiently index high-dimensional data. The article proves that this technique outperforms the usage of the Fourier transformation, since the wavelet transformation provides better approximation of the time series. Through the experiments, it can be concluded that the optimum performance is obtained using 16 to 20 wavelet coefficients. Furthermore, a novel mechanism for reducing the complexity of the calculation for the false alarms removal is proposed. Storing the approximation coefficients of the penultimate level of the decomposition tree, the Euclidean distance between the two sequences is calculated, thus reducing further the number of false alarms before calculating the actual Euclidean distance using the complete time series. The article concludes with a detailed performance evaluation of the proposed similarity retrieval algorithm using data from the Greek stock market and the temperature measurements from Athens. The comparison is done with techniques that use the Haar transform and the R*-Tree, and the proposed algorithm is shown to outperform them.

A data cube has exponential storage and runtime complexities with linearly increasing dimensionality; in addition, a spatial data cube further complicates the issue since it integrates spatial features into a data cube. In this paper, a new data cube approach, named spatial cubing, or simply S-cubing, implements two spatial indexing techniques and two spatial non-relational representations. S-cubing based on shared dimensions is the first non-relational solution designed to support spatial data cubes with continuous dimensions, resolution hierarchies and multiple spatial measures and is capable of running on multi-core computer architectures. S-cubing, based on neighbourhood relationships, implements a new data cube hierarchy algorithm using relationships among cells of a regular grid. Thus, this algorithm creates thematic maps from non-geopolitical regular areas, therein avoiding manual hierarchy definitions. A sequential version is found to be faster than a PostGIS implementation and the parallel version achieved a speedup of 13 with 24 threads.

Suo, A. and Zhang, M., 2015. Sea areas reclamation and coastline change monitoring by remote sensing in coastal zone of Liaoning in China. Aimed to monitor spatial pattern of sea areas reclamation in Liaoning province. Series satellite remote sensing images obtained from 1990 to 2010 were employed to monitor sea areas reclamation and coastline change in coastal zone of Liaoning. Some spatial index was established to describe sea areas reclamation and coastline change by geographical information system, respectively. The results are as follow: Spatial index based on geographical information system are suitable to assess spatial pattern of sea areas reclamation and coastline change. The sea areas reclamation in coastal zone of Liaoning is 35,720.98 hm2 from 1990 to 2010. The sea areas reclamation intension is high in Panjin and Jinzhou with reclamation intension index above 90.0 hm2/km, but low in Huludao, Yingkou and Dalian with reclamation intension index below 20.0 hm2/km. Length of coastline in Liaoning increased and its tortuosity ascend over last 20 years. The increment is 30% in some regions as Jinzhou and Panjin. Exploitation of coastline cause the growth of the length of artificial coastline (dams, bulwark, e.g.), while natural coast line (rock coastline, sandy coastline and muddy coastline) is compressed.

In this work, the assessment of surface and ground water quality of Palladam Taluk, Tiruppur, district, Tamil Nadu, India were carried out using Geographical Information System (GIS) and Modified National Sanitation Foundation -Water Quality Index (MNSF-WQI). Four samples from surface and twenty seven samples from ground water sources were taken from Palladam Taluk, Tiruppur District. In the current study, the surface and ground water samples were analysed for temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), biological oxygen demand (BOD), turbidity, total dissolved solids (TDS), total hardness (TH), faecal coliforms (FC), total phosphate (TP), total nitrate (TN), chlorides (Cl-), sodium (Na+) and fluoride (F-) ions to investigate the suitability of surface and ground water for drinking and agricultural purposes through Geographic information system (GIS) and modified national sanitation foundation water quality index (MNSF-WQI) technique. The concentrations of TH, TDS, Cl- and Na+ were observed to be above the desirable limit of World Health Organization (WHO) guidelines and Bureau of Indian Standards (BIS). whereas F-, BOD, DO, EC, TP, TN, FC and temperature were within the acceptable limits. The GIS-based WQI map analysis indicated that 45% of the study area having good water quality index and the remaining area showed medium quality water. Dyeing and textile industries in the study area are responsible for deteriorating the quality to medium quality of water which was not appropriate for direct utilization and needed prior treatment. There is no detailed report on assessment of the surface and ground water quality of Palladam Taluk in Tamil Nadu using GIS and MNSF-WQI techniques.