USE OF THE ADDRESS REGISTER DATA IN LOCATIONBASED SERVICES

2017 ◽  
Vol 165 (45) ◽  
pp. 66-75
Author(s):  
WALDEMAR IZDEBSKI ◽  
MICHAŁ KURSA
Keyword(s):  
Spatial Data ◽  
Register Data ◽  
Location Based Services ◽  
City Street ◽  
Response Formats ◽  
Address Register ◽  
Address City

Address register is one of the basic and most commonly used spatial data records. One of its uses is finding addresses in location-based services (geocoding), which for the specified address (city, street, number) return the position in the form of coordinates. The paper presents a comparison of two services: ULA (Usługa Lokalizacji Adresów from Geo-System) and OpenLS (available as a part of the Polish national geoportal). The analysis includes both theoretical aspects (amount of data in the resource, its timeliness, documentation, request and response formats), as well as practical - in the form of testing the performance and accuracy of both services.

scholarly journals A Survey on Spatio-temporal Data Analytics Systems

2022 ◽  
Author(s):  
Md Mahbub Alam ◽  
Luis Torgo ◽  
Albert Bifet
Keyword(s):  
Programming Languages ◽  
Spatial Data ◽  
Data Analytics ◽  
Spatial Databases ◽  
Location Based Services ◽  
Temporal Data ◽  
Wide Range ◽  
Data Volume ◽  
Spatio Temporal ◽  
Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

Spatial Data on the Move

2011 ◽  
pp. 103-118
Author(s):  
Wee Hyong Tok ◽  
Stéphane Bresan ◽  
Panagiotis Kalnis ◽  
Baihua Zhengl
Keyword(s):  
Query Processing ◽  
Spatial Data ◽  
Location Based Services ◽  
Spatial Query ◽  
Spatial Query Processing ◽  
Wide Availability ◽  
Research Problems ◽  
Processing Techniques ◽  
Exciting Area ◽  
State Of Art

The pervasiveness of mobile computing devices and wide-availability of wireless networking infrastructure have empowered users with applications that provides location-based services as well as the ability to pose queries to remote servers. This necessitates the need for adaptive, robust, and efficient techniques for processing the queries. In this chapter, we identify the issues and challenges of processing spatial data on the move. Next, we present insights on state-of-art spatial query processing techniques used in these dynamic, mobile environments. We conclude with several potential open research problems in this exciting area.

scholarly journals INDOORGML – A STANDARD FOR INDOOR SPATIAL MODELING

2016 ◽  
Vol XLI-B4 ◽  
pp. 701-704
Author(s):  
Ki-Joune Li
Keyword(s):  
Spatial Data ◽  
Geometric Model ◽  
Spatial Models ◽  
Cell Number ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Space ◽  
Outdoor Spaces ◽  
Core Module ◽  
Outdoor Space

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

scholarly journals Developing a Data Model of Indoor Points of Interest to Support Location-Based Services

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Alexis Richard C. Claridades ◽  
Jiyeong Lee
Keyword(s):  
Spatial Data ◽  
Data Model ◽  
Unified Modeling Language ◽  
Location Based Services ◽  
Accurate Information ◽  
Indoor Location ◽  
Unified Modeling ◽  
Points Of Interest ◽  
Sample Data ◽  
Indoor Spaces

Focus on indoor spatial applications has been rising with the growing interest in indoor spaces. Along with the widespread use of mobile devices and the internet, it has increased demands for indoor location-based services (LBS), demanding more efficient representation and management of indoor spatial data. Indoor points of interest (Indoor POI) data, representing both spaces and facilities located indoors, provide the infrastructure for these services. These datasets are vital in delivering timely and accurate information to users, such as in cases of managing indoor facilities. However, even though there are studies that explore its use across applications and efforts exerted towards the standardization of the data model, most POI development studies have focused on the outdoors and remain underdeveloped in the indoors. In this paper, we propose a spatial-temporal Indoor POI data model to provide direction for the establishment of indoor POI data and to address limitations in currently available data specifications. By exploring how different Indoor POIs are from its outdoor counterparts, particularly on extending its outdoor counterparts’ functions on searching, sharing, and labeling, we describe the data model and its components using the Unified Modeling Language (UML). We perform an SQL-based query experiment to demonstrate the potential use of the data model using sample data.

Spatial Data on the Move

2010 ◽  
pp. 850-864
Author(s):  
Wee Hyong Tok ◽  
Stéphane Bressan ◽  
Panagiotis Kalnis ◽  
Baihua Zheng
Keyword(s):  
Query Processing ◽  
Spatial Data ◽  
Location Based Services ◽  
Spatial Query ◽  
Spatial Query Processing ◽  
Wide Availability ◽  
Research Problems ◽  
Processing Techniques ◽  
Exciting Area ◽  
State Of Art

The pervasiveness of mobile computing devices and wide-availability of wireless networking infrastructure have empowered users with applications that provides location-based services as well as the ability to pose queries to remote servers. This necessitates the need for adaptive, robust, and efficient techniques for processing the queries. In this chapter, we identify the issues and challenges of processing spatial data on the move. Next, we present insights on state-of-art spatial query processing techniques used in these dynamic, mobile environments. We conclude with several potential open research problems in this exciting area.

PSTree-Based Multi-Resolution Model for Buildings Simplification

2012 ◽  
Vol 457-458 ◽  
pp. 1056-1061
Author(s):  
Yu Min Tan ◽  
De Qiang Liu ◽  
Wen Hui Wu
Keyword(s):  
Data Structure ◽  
Urban Environment ◽  
Spatial Data ◽  
Hierarchical Models ◽  
Location Based Services ◽  
Network Environment ◽  
Gis Applications ◽  
Building Models ◽  
Resolution Model

Spatial data navigation speed is a very important element needing to be considered in network based GIS applications, especially for location based services. In this paper, a method to simplify the visualization of buildings in urban environment is proposed. It is based on a modified hierarchical building footprints merging approach and the simplified tree-based data structure, PSTree, is used to store hierarchical models. Thus, by computing the distance from viewpoint, according models can be automatically constructed and rendered, and less detailed building models are used when they are far from current viewpoint. Only the combination of a group of built-in building models is transferred to browsers, and these models are continuous in scale, so visualization effect and speed are both improved under network environment.

scholarly journals A context-driven geospatial web service approach towards the design of situational awareness maps

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Sin-Yi Ho ◽  
Jung-Hong Hong
Keyword(s):  
Spatial Data ◽  
Situational Awareness ◽  
Service Providers ◽  
Location Based Services ◽  
Human Computer Interactions ◽  
Geospatial Information ◽  
Daily Lives ◽  
Data Infrastructure ◽  
Reference Map ◽  
Application Developers

<p><strong>Abstract.</strong> The recent development of internet-based map services has dramatically change how geospatial information is presented to users, novice or professional, for improving the quality and convenience of daily lives. Especially with the innovated internet and computer graphic technology, current map services are designed with comprehensive cartographic considerations and can be simultaneously accessed by multiple clients, with which users can metaphorically establish their understanding towards the phenomena in reality, regardless of where they are. Despite location-based services become increasingly popular, the serviced contents are nevertheless often “static” and the illustration seldom or even fail to adapt to the needs of presenting continuously or periodically changing situations in the real world. From the perspective of intuitive map reading, we argue it is necessary to include the needs of “situational awareness” into the design of map services, so as to improve the human computer interactions in the clients’ application environments. A context-driven approach for the design and mashup of map services are proposed. We first identify three types of map services, namely, reference map services, application-oriented map services and atomic map services. The context of each map services are analysed based on the considerations of theme, time, weather and transportation and described by a set of pre-defined “keywords.” These standardized keywords enable application developers to define rules to select map services with correct contexts and mashup them to meet particular situation needs, e.g., a rainy day map or an emergency medical map services. As the selected map services are already defined with context considerations, their mashup results would be automatically adjusted according to the situations acquired from the clients. By enriching the situational context and enhancing the transparent access of individual map services, a flexible and powerful bridging relationship can be built between the application developers and domain service providers. The proposed approach is hence not only an innovated thinking for designing map services, but should also be seen as a crucial strategy for facilitating interoperable application environments for the ecosystem of the National Spatial Data Infrastructure.</p>

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