Modeling spatial database incremental updating based on base state with amendments

Yan, Lin; Liu, Wanzeng; Chen, Jun

doi:10.1016/j.proeps.2009.09.180

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Many GIS data models have been proposed to incorporate temporal information into spatial databases, including Sequential Snapshots [43], Base State with Amendments [44], Space-time Composite Model [45], Object-oriented Spatiotemporal Model [46], the Event-based Spatiotemporal Data model [47], etc. Several studies have made detailed survey about those spatiotemporal data models [43,48].…”

Section: The Object-oriented Spatiotemporal Data Modelmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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Section: The Object-oriented Spatiotemporal Data Modelmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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“…The types of object updates in the large-scale maps, which will be propagated to objects in the small-scale maps, generally include attribute updates (e.g., land use types, household ownerships) and geometric updates [16][17][18]. The geometric updates in maps of the same scale include various operations of vector polygons [19], such as addition (i.e., objects are newly added), deletion (i.e., objects are removed), aggregation (i.e., several objects are merged into a new one), partition (i.e., one object is split into several new objects), exaggeration (i.e., important objects are enlarged), and geometric transformation (i.e., objects are reshaped and transformed to another projection).…”

Section: Brief Description Of the Concept Of Update Propagationmentioning

confidence: 99%

Propagating Updates of Residential Areas in Multi-Representation Databases Using Constrained Delaunay Triangulations

Zhang

Guo

Huang

et al. 2016

IJGI

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Updating topographic maps in multi-representation databases is crucial to a number of applications. An efficient way to update topographic maps is to propagate the updates from large-scale maps to small-scale maps. Because objects are often portrayed differently in maps of different scales, it is a complicated process to produce multi-scale topographic maps that meet specific cartographical criteria. In this study, we propose a new approach to update small-scale maps based on updated large-scale maps. We first group spatially-related objects in multi-scale maps and decompose the large-scale objects into triangles based on constrained Delaunay triangulation. We then operate the triangles and construct small-scale objects by accounting for cartographical generalization rules. In addition, we apply the Tabu Search algorithm to search for the optimal sequences when constructing small-scale objects. A case study was conducted by applying the developed method to update residential areas at varied scales. We found the proposed method could effectively update small-scale maps while maintaining the shapes and positions of large-scale objects. Our developed method allows for parallel processing of update propagation because it operates grouped objects together, thus possesses computational advantages over the sequential updating method in areas with high building densities. Although the method proposed in this study requires further tests, it shows promise with respect to automatic updates of polygon data in the multi-representation databases.

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“…the data delta between old version and new version of the same database. So providing data delta not the whole new updated database can overcome above mentioned problems in the batch method [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Reasons of the Incremental Information in the Updating Spatial Database

Zhu

Sun

2011

Computer and Computing Technologies in Agriculture IV

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Abstract. In order to quickly extract and distribute the incremental information of spatial database, this work analyses the reasons of incremental information of spatial database with the instance of residential feature. Firstly, a new concept of increment is proposed based on the spatio-temporal change type and data delta. The change type describes the semantics of increment information and data delta describes the content of increment information. Based on this concept, the types of residential change and relevant data deltas are put forward. There are two catalogs of change type, i.e. subjective change and objective change. The subjective change can be detailedly divided into 3 small change types and the objective change can be detailedly divided into 12 small change types. Additionally, the incremental information resulted from the objective change is formalized. Based on the above formalized changes, the automatic extraction of incremental information can be expediently programmed. The classifying result was applied to the incremental information automatic extracting system of 1.250,000 topographic databases.

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Modeling spatial database incremental updating based on base state with amendments

Cited by 5 publications

References 4 publications

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Propagating Updates of Residential Areas in Multi-Representation Databases Using Constrained Delaunay Triangulations

Reasons of the Incremental Information in the Updating Spatial Database

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