scholarly journals TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping

2019 ◽  
Vol 8 (11) ◽  
pp. 513 ◽  
Author(s):  
Luiz Fernando F. G. Assis ◽  
Karine Reis Ferreira ◽  
Lubia Vinhas ◽  
Luis Maurano ◽  
Claudio Almeida ◽  
...  
Keyword(s):  
Spatial Data ◽  
Web Application ◽  
Data Analytics ◽  
Large Scale ◽  
Computer Network ◽  
Service Architecture ◽  
Data Infrastructure ◽  
Spatio Temporal ◽  
Spatial Interfaces ◽  
The Government

The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems.

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.

scholarly journals Implementing an open source spatio-temporal search platform for Spatial Data Infrastructures

2016 ◽  
Author(s):  
Paolo Corti ◽  
Benjamin G Lewis ◽  
Athanasios Tom Kralidis ◽  
Ntabathia Jude Mwenda
Keyword(s):  
Open Source ◽  
Search Engine ◽  
Language Processing ◽  
Spatial Data ◽  
Spatial Information ◽  
National Endowment ◽  
Data Infrastructure ◽  
Advanced Search ◽  
Data Infrastructures ◽  
Spatio Temporal

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query, and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use “any means necessary” to get users to the resources they need quickly and efficiently. These techniques may include features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations, feedback mechanisms based on log mining, usage statistic gathering, and many others. In this paper we will be focusing on improving geospatial search with a search engine platform that uses Lucene, a Java-based search library, at its core. In work funded by the National Endowment for the Humanities, the Centre for Geographic Analysis (CGA) at Harvard University is in the process of re-engineering the search component of its public domain SDI (WorldMap http://worldmap.harvard.edu ) which is based on the GeoNode platform. In the process the CGA has developed Harvard Hypermap (HHypermap), a map services registry and search platform independent from WorldMap. The goal of HHypermap is to provide a framework for building and maintaining a comprehensive registry of web map services, and because such a registry is expected to be large, the system supports the development of clients with modern search capabilities such as spatial and temporal faceting and instant previews via an open API. Behind the scenes HHypermap scalably harvests OGC and Esri service metadata from distributed servers, organizes that information, and pushes it to a search engine. The system monitors services for reliability and uses that to improve search. End users will be able to search the SDI metadata using standard interfaces provided by the internal CSW catalogue, and will benefit from the enhanced search possibilities provided by an advanced search engine. HHypermap is built on an open source software source stack.

scholarly journals Implementing an open source spatio-temporal search platform for Spatial Data Infrastructures

2016 ◽  
Author(s):  
Paolo Corti ◽  
Benjamin G Lewis ◽  
Tom Kralidis ◽  
Jude Mwenda
Keyword(s):  
Open Source ◽  
Search Engine ◽  
Language Processing ◽  
Spatial Data ◽  
Search Engines ◽  
Spatial Information ◽  
Data Infrastructure ◽  
Advanced Search ◽  
Data Infrastructures ◽  
Spatio Temporal

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide the most efficient and flexible way to use spatial information. One of the key software components of a SDI is the catalogue service, needed to discover, query and manage the metadata. Catalogue services in a SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard, that defines common interfaces to access the metadata information. A search engine is a software system able to perform very fast and reliable search, with features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting and many others. The Centre of Geographic Analysis (CGA) at Harvard University is trying to integrate within its public domain SDI (named WorldMap), the benefits of both worlds (OGC catalogues and search engines). Harvard Hypermap (HHypermap) is a component that will be part of WorldMap, totally built on an open source stack, implementing an OGC catalogue, based on pycsw, to provide access to metadata in a standard way, and a search engine, based on Solr/Lucene, to provide the advanced search features typically found in search engines.

scholarly journals Implementing an open source spatio-temporal search platform for Spatial Data Infrastructures

2016 ◽  
Author(s):  
Paolo Corti ◽  
Benjamin G Lewis ◽  
Tom Kralidis ◽  
Jude Mwenda
Keyword(s):  
Open Source ◽  
Search Engine ◽  
Language Processing ◽  
Spatial Data ◽  
Search Engines ◽  
Spatial Information ◽  
Data Infrastructure ◽  
Advanced Search ◽  
Data Infrastructures ◽  
Spatio Temporal

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide the most efficient and flexible way to use spatial information. One of the key software components of a SDI is the catalogue service, needed to discover, query and manage the metadata. Catalogue services in a SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard, that defines common interfaces to access the metadata information. A search engine is a software system able to perform very fast and reliable search, with features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting and many others. The Centre of Geographic Analysis (CGA) at Harvard University is trying to integrate within its public domain SDI (named WorldMap), the benefits of both worlds (OGC catalogues and search engines). Harvard Hypermap (HHypermap) is a component that will be part of WorldMap, totally built on an open source stack, implementing an OGC catalogue, based on pycsw, to provide access to metadata in a standard way, and a search engine, based on Solr/Lucene, to provide the advanced search features typically found in search engines.

scholarly journals Software to compare images of the vegetation index obtained by satellite devices and unmanned aircraft

2020 ◽  
Vol 23 (4) ◽  
pp. 63-70
Author(s):  
M. Yu. Kataev ◽  
◽  
M. O. Krylov ◽  
P. P. Geiko ◽  
◽  
...  
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Spatial Information ◽  
Measurement Data ◽  
Unmanned Aircraft ◽  
Software System ◽  
Temporal Data ◽  
Sources Of Information ◽  
Data Infrastructure ◽  
Spatio Temporal

At present, the practice of supporting many types of human activities requires the use of the spatial data infrastructure. Such an infrastructure integrates spatio-temporal sets from many sources of information within itself, providing the user with various types of processing, analysis and visualization methods. This article describes the architecture of the software system and the processes for managing sets of spatio-temporal data to solve agricultural problems. Measurement data using multispectral satellite systems, unmanned aerial vehicles (UAVs), as well as a priori information (meteorology, agrochemical information, etc.) are taken as input information. The User of the Software System is provided with the opportunity to control the spatial information of the territory of agricultural fields, sets of temporal data from various spatial data. An important achievement of the work is the combination of the results of satellite and UAV images according to the controlled parameters, that makes possible to expand the area of use of UAVs and verify them. The results of real data processing are presented.

scholarly journals How is public transit in the megacity peripheral relocatees’ area in China? Captive transit rider and dynamic modal accessibility gap analytics in a peripheral large-scale residential area in Shanghai, China

2020 ◽  
Vol 13 (1) ◽  
pp. 1-21
Author(s):  
Jinping Guan ◽  
Kai Zhang ◽  
Shuang Zhang ◽  
Yizhou Chen
Keyword(s):  
Data Analytics ◽  
Large Scale ◽  
Public Transit ◽  
Central City ◽  
Subjective Evaluation ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Transit Planning ◽  
Covered Area ◽  
The Government

In the process of Chinese megacity suburbanization, central-city substandard housing is demolished. The government relocates residents to megacity peripheral relocatees’ areas. So far, few studies have focused on captive transit riders and analyzed the dynamic points of interest (POI) accessibility by public transit compared to the private mode in these areas. To fill this gap, this study conducts a survey in Jinhexincheng, one of these areas in Shanghai, China; analyzes captive-transit riders with multiple models; and computes the dynamic modal accessibility gap (DMAG) of public transit and private travel mode using multi-source heterogeneous data. Results show that 71.77% of transit-rider samples acknowledge they “have no other choice and have to travel by transit.” These captive transit riders are more often older, female, non-working, without a driving license, and with more complaints toward public transport. Subjective transit evaluation’s ordinal regression models show that waiting time, speed, operating hours, and number of lines/stops contribute to the low transit subjective evaluation. These things should be given a high priority in transit improvement. As for the captive transit riders, transit’s transfer and bicycle availability should be improved. Using big data analytics, a more fine-grained scale is brought in by computing a DMAG index. It shows a person mostly has a better POI accessibility by private mode for the 30-minute, real-travel-covered area for 24 hours of the average day. For the 60-minute, real-travel-covered area, public transit mostly has a better POI accessibility. This study supports transit planning and decision-making for megacity peripheral relocatees’ areas using multi-source heterogeneous data analytics.

scholarly journals Implementing an open source spatio-temporal search platform for Spatial Data Infrastructures

2016 ◽  
Author(s):  
Paolo Corti ◽  
Benjamin G Lewis ◽  
Athanasios Tom Kralidis ◽  
Ntabathia Jude Mwenda
Keyword(s):  
Open Source ◽  
Search Engine ◽  
Language Processing ◽  
Spatial Data ◽  
Spatial Information ◽  
National Endowment ◽  
Data Infrastructure ◽  
Advanced Search ◽  
Data Infrastructures ◽  
Spatio Temporal

A Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query, and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use “any means necessary” to get users to the resources they need quickly and efficiently. These techniques may include features such as full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations, feedback mechanisms based on log mining, usage statistic gathering, and many others. In this paper we will be focusing on improving geospatial search with a search engine platform that uses Lucene, a Java-based search library, at its core. In work funded by the National Endowment for the Humanities, the Centre for Geographic Analysis (CGA) at Harvard University is in the process of re-engineering the search component of its public domain SDI (WorldMap http://worldmap.harvard.edu ) which is based on the GeoNode platform. In the process the CGA has developed Harvard Hypermap (HHypermap), a map services registry and search platform independent from WorldMap. The goal of HHypermap is to provide a framework for building and maintaining a comprehensive registry of web map services, and because such a registry is expected to be large, the system supports the development of clients with modern search capabilities such as spatial and temporal faceting and instant previews via an open API. Behind the scenes HHypermap scalably harvests OGC and Esri service metadata from distributed servers, organizes that information, and pushes it to a search engine. The system monitors services for reliability and uses that to improve search. End users will be able to search the SDI metadata using standard interfaces provided by the internal CSW catalogue, and will benefit from the enhanced search possibilities provided by an advanced search engine. HHypermap is built on an open source software source stack.

scholarly journals ODISHA SPATIAL DATA INFRASTRUCTURE (OSDI) – ITS DATA MODEL, META DATA AND SHARING POLICY

2018 ◽  
Vol IV-5 ◽  
pp. 21-28
Author(s):  
P. K. Parida ◽  
S. Tripathi
Keyword(s):  
State Government ◽  
Spatial Data ◽  
Data Model ◽  
Good Governance ◽  
Spatial Data Infrastructure ◽  
Public Funds ◽  
Meta Data ◽  
Data Infrastructure ◽  
Data Policy ◽  
The Government

<p><strong>Abstract.</strong> Recognizing the potential utility and importance of a large quantity of spatial data generated using public funds by the Government Departments, organizations and institutions of the State for good governance and taking into consideration that most of such spatial data remains inaccessible to common citizen although most of such data may be unrestricted and not sensitive in nature and also most of such data generated at different State Government Departments do not have compatibility due to lack of common standards and non-interoperability and further taking note of that Government of India framed the “National Data Sharing and Accessibility Policy (NDSAP)”, National Map Policy (2005) and “Remote Sensing Data Policy (RSDP- 2001 and 2011)” to spell out sharing principles of information generated using public funds, Government of Odisha has institutionalised “Odisha Spatial Data Infrastructure(OSDI)”, in the line of National Spatial Data Infrastructure(NSDI)”. The Government of Odisha gazetted “Odisha Spatial Data Policy (OSDP)” in 22nd August 2015, in the line of NDSAP, to institute a policy frame work to facilitate sharing of such Government owned data through OSDI, in open format, for supporting sustainable and inclusive governance and effective planning, implementation and monitoring of developmental programmes, managing and mitigating disasters and scientific research aiding informed decisions, for public good. The OSDI has already been operational and made live.</p><p>This paper highlights the Data Model, Meta Data Standard and Sharing Policy adopted in OSDI, apart from other institutional / operational issues in smooth grounding and operationalisation of the OSDI in a State framework.</p>

scholarly journals Evaluation of accuracy of digital map data via multiple comparisons

2016 ◽  
Vol 64 (4) ◽  
pp. 799-805
Author(s):  
A. Doskocz ◽  
W. Rejchel
Keyword(s):  
European Community ◽  
Spatial Data ◽  
Large Scale ◽  
Multiple Comparisons ◽  
Data Sets ◽  
Positional Accuracy ◽  
Statistical Tools ◽  
Digital Map ◽  
Data Infrastructure ◽  
Map Data

Abstract Digital map data sets (or geo-databases) are an important part of the spatial data infrastructure (SDI) of the European Community. Different methods of producing large-scale map data are described in the paper, and the aim is to compare the accuracy of these methods. Our analysis is based on statistical tools belonging to the multiple comparisons theory. The first method is the well-known analysis of variance (ANOVA), and the second one is the rank-based method. The latter approach, which is rarely used in geodetic research, allows us to determine the order of the considered methods with respect to the positional accuracy of digital map data that they produce. Using this approach, one can identify the least accurate set of map data or a fragment of a map that should be updated by a new direct survey. The rank-based methods can also be rather easily applied to other technical (engineering) disciplines, e.g. geodesy and cartography.

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