scholarly journals A QUADTREE SPATIAL INDEX METHOD WITH INCLUSION RELATIONS FOR THE INCREMENTAL UPDATING OF VECTOR LANDCOVER DATABASE

2018 ◽  
Vol XLII-4 ◽  
pp. 757-761
Author(s):  
X. G. Zhou ◽  
H. S. Wang
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Index Method ◽  
Incremental Updating ◽  
Indexing Methods ◽  
Data Indexing ◽  
Inclusion Relations ◽  
Complex Polygon

<p><strong>Abstract.</strong> In vector landcover database, there are a lot of complex polygons with many holes, even nesting holes. In the incremental updating (i.e., using the change-only information to update the land cover database), a new changed parcel usually has 2-dimensional intersections (e.g., overlap, cover, equal and inside, etc.) with several existing regions, automatic updating operations need to identify the affected objects for the new changes at first. If the existing parcels include complex polygons (i.e., the polygon with holes), it is still needed to determine if there are 2-dimensional intersections between the new changed polygon and each holes of the involved complex polygons. The relation between the complex polygon and its holes has not been presented in the current spatial data indexing methods, only the MBB (Minimum Bounding Box) of the exterior ring of the complex polygon has been stored, the non-involved holes can not be filtered at the first step of spatial access methods. As the refinement geometric operation is costly, therefore the updating process for the complex polygons is very complicated and low efficient using the current spatial data indexing methods. In order to solve this problem, an improved quadtree spatial index method is presented in this paper. In this method, the polygons is divided to two categories according to the relations with the quadrant axes, i.e., disjoint to the axes and intersect with the axes. The intersect polygons are still divided to 5 cases according to the intersection position among the polygons and the different level quadrant axes. The intersection polygons are stored in the different level root nodes in our index tree, and five buckets denoted as <i>XpB, XnB, YpB, YnB, XYB</i> are used to store the polygons intersecting the different level quadrant axes respectively. The polygons disjoint to all quadrant axes are stored in the leaf nodes in this method. The authors developed the spatial index structure with inclusion relations and the algorithms of the corresponding index operations (e.g., insert, delete and query) for the complex polygons. The effectiveness of the improved index is verified by an experiment of land cover data incremental updating. Experimental results show that the proposed index method is significantly more efficient than the traditional quadtree index in terms of spatial query efficiency, and the time efficiency of the incremental updating is increased about 3 times using the proposed index method than that using the traditional quadtree index.</p>

A Mobile Storage System for Massive Spatial Data

2014 ◽  
Vol 962-965 ◽  
pp. 2730-2734
Author(s):  
Zhen Song ◽  
Jian Chen ◽  
Jiu Yan Ye
Keyword(s):  
Mobile Devices ◽  
Data Storage ◽  
Spatial Data ◽  
Storage System ◽  
Hierarchical Classification ◽  
Spatial Index ◽  
Index Structure ◽  
Real Time Visualization ◽  
Spatial Data Organization ◽  
Low Efficiency

This paper aims at the solution to problems of large amounts of data storage, low efficiency calculate and poor user experience which actually exists in GIS application on mobile devices. In order to solve these issues we use GIS technologies including spatial data organization, map browser, and spatial index. We focused on the research of how to effectively utilize system resources and rational and efficient out organize the spatial data. What’s more, we improved the R-tree indexing algorithm to establish a rapid spatial index structure and used hierarchical classification techniques to optimize the efficiency of the real-time visualization of spatial data for mobile devices.

Research on Cache Quadtree: A Cache Index for Spatial Data Based on Intelligent Material System

2011 ◽  
Vol 63-64 ◽  
pp. 795-799
Author(s):  
Zhong Jie Zhang ◽  
De Peng Zhao ◽  
De Qiang Wang
Keyword(s):  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Material System ◽  
Spatial Indexing ◽  
Data Query ◽  
Intelligent Material

This paper presents a new spatial index structure - cache quadtree. Cache quadtree combined with the advantages of spatial indexing and caching. According to the characteristics of spatial data, query uses the previous query results as much as possible, only the necessary queries are performed on the server. Meanwhile, full use of query results in the cache tree; reduce the server's query and its query range. Our paper gives the cache quadtree structure and the key algorithm in detail.

LAZY R-tree: The R-tree with lazy splitting algorithm

2019 ◽  
Vol 46 (2) ◽  
pp. 243-257
Author(s):  
Yang Yang ◽  
Pengwei Bai ◽  
Ningling Ge ◽  
Zhipeng Gao ◽  
Xuesong Qiu
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Splitting Method ◽  
Hash Table ◽  
Spatial Index ◽  
Index Structure ◽  
Splitting Algorithm ◽  
Tree Construction ◽  
Tree Index

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k-nearest neighbour (kNN) queries.

Study on the Implementation of Quadtree Index on E-Chart System

2011 ◽  
Vol 63-64 ◽  
pp. 627-632
Author(s):  
Zhong Jie Zhang ◽  
De Peng Zhao ◽  
De Qiang Wang
Keyword(s):  
Experimental Data ◽  
Mobile Devices ◽  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Index Structures ◽  
Application Development ◽  
Electronic Chart ◽  
Comparison And Analysis

As embedded and mobile devices application development and many micro-computing devices need to run the e-chart system. The tree spatial index structures can efficiently organize and retrieve spatial data. This paper discusses the index to organize and retrieve e-chart data through the quadtree. We discussed the electronic chart in the two typical quadtree on the index structure, and give the comparison and analysis of experimental data.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

A New Quadtree-Based Skyline Query Algorithm

2014 ◽  
Vol 981 ◽  
pp. 175-178
Author(s):  
Run Tao Liu ◽  
Yuan Jing Chen ◽  
Da Yong Cao ◽  
De Yu Liu
Keyword(s):  
Detailed Analysis ◽  
Spatial Data ◽  
Index Structure ◽  
Skyline Query ◽  
Set Up ◽  
Query Algorithm

In this paper, the index structure, PR-quadtree for spatial data, is used to store data for a database. The properties of the quadtree are studied. With the properties prunning rules are set up for searching the Skyline set of the data stored in the quadtree. Through detailed analysis for the tree the method of finding some approximate skyline points is designed, by which a new skyline searching algorithm is given. The new algorithm is more effective.

A Research of Matching Algorithm and Strategy on Entity with Complex Area

2012 ◽  
Vol 209-211 ◽  
pp. 252-255
Author(s):  
Li Guo ◽  
Hai Ying Zheng ◽  
Yong Hong Wang ◽  
Bin Zhang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Data Matching ◽  
Large Scale Data ◽  
Polygon Area ◽  
Spatial Data Integration ◽  
One And Many ◽  
Complex Polygon ◽  
Scale Data ◽  
Matching Relation

Data matching technology is a key technology for spatial data integration and fusion. This paper represents a solution to the complex polygon area, defines the area overlapped rate in the aspect of geometric measure, presents the data matching idea based on area overlapped rate .Then, this paper discusses and realizes the data matching relation of area elements including one to one , many to one and many to many. At last, region targets are set as the study object, large scale data are taken for example. We draw the conclusion: this algorithm is efficient.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

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