Updating an Adaptive Spatial Index

The rapid accessibility of information is an important requirement of the hypermap systems or the geographical informations systems. But the massive volumes of data oblige us to store it in secondary memory which greatly slows down the access time. Thus the indexing technique, which determines the way of secondary memory access, is an essential point in hypermap systems. There are several researches on spatial indexing techniques, but due to the lack of comparative studies which must be based on the analysis of the nature of the spatial data, it is difficult to say that one is better than another.

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.

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.

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.

Similarity search in high-dimensional metric spaces is routinely used in many applications including content-based image retrieval, bioinformatics, data mining, and recommender systems. Search can be accelerated by the use of an index. However, constructing a high-dimensional index can be quite expensive and may not pay off if the number of queries against the data is not large. In these circumstances, it is beneficial to construct an index adaptively , while responding to a query workload. Existing work on multidimensional adaptive indexing partitions space into orthotopes (i.e., hyperrectangular units). This approach, however, is highly ineffective in high-dimensional spaces. In this paper, we propose AV-tree: an alternative method for adaptive high-dimensional indexing that exploits previously computed distances, using query centers as vantage points. Our experimental study shows that AV-tree yields cumulative cost for the first several hundred or even thousand queries much lower than that of pre-built indices. After thousands of queries, the per-query performance of the AV-tree converges or even surpasses that of the state-of-the-art MVP-tree. Arguably, our approach is commendable in environments where the expected number of queries is not large while there is a need to start answering queries as soon as possible, such as applications where data are updated frequently and past data soon become obsolete.

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

Scientists are facing the data deluge in the scientific explorations. Big data are collected by the scientific instruments and experiments. The data are usually multidimensional arrays and stored in many files. Distributed computing techniques such as MapReduce make exploring the large datasets practical. The index is a well-known measure to shorten the query processing duration. Most of existing indexing methods need a full load of the raw data to build the index. In this paper, we proposed a distributed adaptive indexing method for the distributed array-oriented query processing. Our method does not require a full scan of the array data. For each subarray accessed by a subtask, we divide the array into multiple logical blocks with a proper block size. The normal processing routine is executed when handling a query. Meanwhile, the index for the blocks accessed by the query is built at a low cost. So the whole index grows along with processing queries. This incremental manner exploits the accessed data of historical queries and eliminates the long load procedure. The experiments show that our adaptive indexing implemented over Hadoop and Hive is effective for accelerating array-oriented query processing without introducing much overhead.

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.

Adaptive indexing is characterized by the partial creation and refinement of the index as side effects of query execution. Dynamic or shifting workloads may benefit from preliminary index structures focused on the columns and specific key ranges actually queried --- without incurring the cost of full index construction. The costs and benefits of adaptive indexing techniques should therefore be compared in terms of initialization costs, the overhead imposed upon queries, and the rate at which the index converges to a state that is fully-refined for a particular workload component. Based on an examination of database cracking and adaptive merging, which are two techniques for adaptive indexing, we seek a hybrid technique that has a low initialization cost and also converges rapidly. We find the strengths and weaknesses of database cracking and adaptive merging complementary. One has a relatively high initialization cost but converges rapidly. The other has a low initialization cost but converges relatively slowly. We analyze the sources of their respective strengths and explore the space of hybrid techniques. We have designed and implemented a family of hybrid algorithms in the context of a column-store database system. Our experiments compare their behavior against database cracking and adaptive merging, as well as against both traditional full index lookup and scan of unordered data. We show that the new hybrids significantly improve over past methods while at least two of the hybrids come very close to the "ideal performance" in terms of both overhead per query and convergence to a final state.

Lines and Polygons Overlay Analysis (LP-OA) is an important analysis method that has been widely used in geographic information systems (GIS). In this study, a new algorithm, Uniform Spatial Grid Indexing (USGI), is proposed to study LP-OA. By applying this method, the theoretical position is evaluated and the theoretical results are verified, suggesting that this algorithm can contribute to the software development in commercial GIS.

Implementing spatial segregation measures in GIS

On multi-scale display of geometric objects

Abstract. Society and economy are only two of the dimensions of vulnerability. This paper aims to elucidate the state of the art of data sources, spatial variables, indicators, methods, indexes and tools for the spatial assessment of socio-economic vulnerability (SEV) related to geohazards. This review was first conducted in December 2018 and re-run in March 2020 for the period between 2010 and 2020. The gross number of articles reviewed was 27, from which we identified 18 relevant references using a revised search query and six relevant references identified using the initial query, giving a total sample of 24 references. The most common source of data remains population censuses. The most recurrent spatial variable used for the assessment of SEV is households without basic services, while critical facilities are the most frequent spatial category. Traditional methods have been combined with more innovative and complex methods to select and weight spatial indicators and develop indices. The Social Vulnerability Index (SoVI®) remains the benchmark for the assessment of SEV and a reference for its spatial assessment. Geographic information systems (GIS) is the most common tool for conducting a spatial assessment of SEV regarding geohazards. For future spatial assessments of SEV regarding geohazards, we recommend considering 3-D spatial indexes at the microscale at the urban level and involving the community in the assessments.

Efficient processing of spatial joins with DOT-based indexing

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.