Consistency in Spatial Databases

2005 ◽  
pp. 93-98 ◽  
Author(s):  
M. Andrea Rodríguez-Tastets
Keyword(s):  
Spatial Data ◽  
Computer Aided Design ◽  
Large Scale ◽  
Spatial Databases ◽  
Data Sets ◽  
Navigation Systems ◽  
Large Scale Integration ◽  
Spatial Data Sets ◽  
Scale Integration ◽  
Aided Design

During the past several years, traditional databases have been enhanced to include spatially referenced data. Spatial database management (SDBM) systems aim at providing models for the efficient manipulation of data related to space. Such type of manipulation is useful for any type of applications based on large spatial data sets, such as computer-aided design (CAD), very large scale integration (VLSI), robotics, navigation systems, and image processing.

Spatial Network Databases

2009 ◽  
pp. 307-315 ◽  
Author(s):  
Michael Vassilakopoulos
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Computer Aided Design ◽  
Geographical Information Systems ◽  
Large Scale ◽  
Spatial Databases ◽  
Query Language ◽  
Geographical Information ◽  
Data Types ◽  
Large Scale Integration

A Spatial Database is a database that offers spatial data types, a query language with spatial predicates, spatial indexing techniques, and efficient processing of spatial queries. All these fields have attracted the focus of researchers over the past 25 years. The main reason for studying spatial databases has been applications that emerged during this period, such as Geographical Information Systems, Computer-Aided Design, Very Large Scale Integration design, Multimedia Information Systems, and so forth. In parallel, the field of temporal databases, databases that deal with the management of timevarying data, attracted the research community since numerous database applications (i.e., Banking, Personnel Management, Transportation Scheduling) involve the notion of time.

Enhancing the Efficiency of ICT by Spatial Data Interoperability

2011 ◽  
pp. 1182-1197
Author(s):  
Otakar Cerba ◽  
Karel Charvat ◽  
Jan Jezek ◽  
Stepan Kafka
Keyword(s):  
Information And Communication Technologies ◽  
Spatial Data ◽  
Data Sets ◽  
Navigation Systems ◽  
Sources Of Information ◽  
Data Interoperability ◽  
Data Description ◽  
International Projects ◽  
Green Ict ◽  
Spatial Data Sets

In the present world of information and communication technologies (ICT) “Green ICT” represents a topic of immense interest. The meaning, sense and scope of Green ICT are quite varied and very wide. Hardware technologies, for example (virtualization of hardware) and corresponding methods are considered initiatives towards environment protection and sustainable growth. At the same time, however, improved development and implementation of existing tools influencing environment by implication (for example due to reducing travel costs or energy savings) are very important in terms of Green ICT. ICT solutions could also work as a device or medium of implementation of new environmentally friendly methods, for instance in agriculture or industry. Spatial data or data with a direct or indirect reference to a specific location or geographic area (INSPIRE Registry, 2009), like digital maps, data in navigation tools, are a significant means of correlating otherwise disparate sources of information. This chapter tries to show the relationship of spatial data and how it can benefit Green ICT. This relationship is vital, as spatial data plays a very important role in system and application (e.g. Geographic Information Systems) with the potential for making direct impact on environmental protection. Spatial data continues to be an integral part of common equipment like mobile phones, car navigation systems and computers. The numbers of these gadgets are constantly growing and so is the corresponding volume of spatial data sets. Within the context of this rapid growth, the costs of data capture, management, updating, processing and distribution are increasing. For example the operation of servers containing the same spatial data sets is energy-consuming and results in burdening the influence on environment. Spatial data sharing, re-use and possibilities of interconnection of existing spatial data sources pose a solution. Therefore, the spatial data interoperability assurance (e.g. by private spatial data providers, state administration etc.) is required. The spatial data interoperability enables more efficient management and use of spatial data sets and achieving of desired savings.The principles of spatial data interoperability are described in the first part of this document. Emphasis is put on spatial data heterogeneities as the main problem of spatial data interoperability. Moreover, technologies focused on elimination of spatial data heterogeneities are discussed here. Subsequent paragraphs introduce selected instruments (metadata, schema languages, ontologies) which are based on data description and support data interoperability. The last section of this document is composed of examples of several international projects focused on spatial data description and processing of well-described spatial data through web services.

Enhancing the Efficiency of ICT by Spatial Data Interoperability

2011 ◽  
pp. 301-316
Author(s):  
Otakar Cerba ◽  
Karel Charvat ◽  
Jan Jezek ◽  
Stepan Kafka
Keyword(s):  
Information And Communication Technologies ◽  
Spatial Data ◽  
Data Sets ◽  
Navigation Systems ◽  
Sources Of Information ◽  
Data Interoperability ◽  
Data Description ◽  
International Projects ◽  
Green Ict ◽  
Spatial Data Sets

In the present world of information and communication technologies (ICT) “Green ICT” represents a topic of immense interest. The meaning, sense and scope of Green ICT are quite varied and very wide. Hardware technologies, for example (virtualization of hardware) and corresponding methods are considered initiatives towards environment protection and sustainable growth. At the same time, however, improved development and implementation of existing tools influencing environment by implication (for example due to reducing travel costs or energy savings) are very important in terms of Green ICT. ICT solutions could also work as a device or medium of implementation of new environmentally friendly methods, for instance in agriculture or industry. Spatial data or data with a direct or indirect reference to a specific location or geographic area (INSPIRE Registry, 2009), like digital maps, data in navigation tools, are a significant means of correlating otherwise disparate sources of information. This chapter tries to show the relationship of spatial data and how it can benefit Green ICT. This relationship is vital, as spatial data plays a very important role in system and application (e.g. Geographic Information Systems) with the potential for making direct impact on environmental protection. Spatial data continues to be an integral part of common equipment like mobile phones, car navigation systems and computers. The numbers of these gadgets are constantly growing and so is the corresponding volume of spatial data sets. Within the context of this rapid growth, the costs of data capture, management, updating, processing and distribution are increasing. For example the operation of servers containing the same spatial data sets is energy-consuming and results in burdening the influence on environment. Spatial data sharing, re-use and possibilities of interconnection of existing spatial data sources pose a solution. Therefore, the spatial data interoperability assurance (e.g. by private spatial data providers, state administration etc.) is required. The spatial data interoperability enables more efficient management and use of spatial data sets and achieving of desired savings.The principles of spatial data interoperability are described in the first part of this document. Emphasis is put on spatial data heterogeneities as the main problem of spatial data interoperability. Moreover, technologies focused on elimination of spatial data heterogeneities are discussed here. Subsequent paragraphs introduce selected instruments (metadata, schema languages, ontologies) which are based on data description and support data interoperability. The last section of this document is composed of examples of several international projects focused on spatial data description and processing of well-described spatial data through web services.

scholarly journals MINING CO-LOCATION PATTERNS WITH CLUSTERING ITEMS FROM SPATIAL DATA SETS

2018 ◽  
Vol XLII-3 ◽  
pp. 2505-2509
Author(s):  
G. Zhou ◽  
Q. Li ◽  
G. Deng ◽  
T. Yue ◽  
X. Zhou
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Location Pattern ◽  
Spatial Feature ◽  
Geographic Space ◽  
Location Patterns ◽  
Spatial Data Sets

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

Landscape analysis of risk factors for white pine blister rust in the Mixed Forest Province of Minnesota, U.S.A.

2002 ◽  
Vol 32 (9) ◽  
pp. 1639-1650 ◽  
Author(s):  
Mark A White ◽  
Terry N Brown ◽  
George E Host
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Mixed Forest ◽  
Data Sets ◽  
White Pine Blister Rust ◽  
White Pine ◽  
Blister Rust ◽  
Pine Regeneration ◽  
Lake States ◽  
Spatial Data Sets

The abundance of eastern white pine (Pinus strobus L.) has been significantly reduced in northeastern Minnesota over the past 120 years. White pine blister rust (WPBR), a commonly lethal fungal disease of white pine, was introduced in Minnesota in approximately 1914 and now, along with other factors such as herbivore browsing, poses a major challenge to attempts to reestablish white pines in the region. A map delineating broad WPBR hazard zones for the Lake States region was prepared in 1964. We created a higher resolution map that estimates the spatial variability of WPBR hazard in the Laurentian Mixed Forest Province of Minnesota using modern geographic information system techniques and readily available spatial databases. The new map has significantly higher resolution than the old and demonstrates that even within areas previously classified as "high hazard", there are significant acreages of "low-hazard" areas where white pine regeneration may be possible. Our analyses are consistent with previous work in the Lake States region, showing that climate, topographic characteristics, and distance from water bodies and wetlands have a strong influence on WPBR infection hazard. We also present methods for analyzing forest conditions at regional scales using commonly available spatial data sets.

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