Supporting Geo-Ontology Engineering Through Spatial Data Analytics

2016 ◽  
pp. 556-571
Author(s):  
Gloria Re Calegari ◽  
Emanuela Carlino ◽  
Irene Celino ◽  
Diego Peroni
Keyword(s):  
Spatial Data ◽  
Data Analytics ◽  
Ontology Engineering

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 Analytics

2017 ◽  
pp. 149-157 ◽  
Author(s):  
Harvey J. Miller
Keyword(s):  
Spatial Data ◽  
Data Analytics

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 Welcome to a New Generation of Entertainment: Amazon Web Services and the Normalization of Big Data Analytics and RFID Tracking

2019 ◽  
Vol 17 (1/2) ◽  
pp. 169-175 ◽  
Author(s):  
Justin Joseph Grandinetti
Keyword(s):  
Big Data ◽  
Web Services ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Spatial Data ◽  
Data Analytics ◽  
National Football League ◽  
Big Data Analytics ◽  
Data Driven ◽  
Amazon Web Services

The 2017 partnership between the National Football League (NFL) and Amazon Web Services (AWS) promises novel forms of cutting-edge real-time statistical analysis through the use of both radio frequency identification (RFID) chips and Amazon’s cloud-based machine learning and data-analytics tools. This use of RFID is heralded for its possibilities: for broadcasters, who are now capable of providing more thorough analysis; for fans, who can experience the game on a deeper analytical level using the NFL’s Next Gen Stats; and for coaches, who can capitalize on data-driven pattern recognition to gain a statistical edge over their competitors in real-time. In this paper, we respond to calls for further examination of the discursive positionings of RFID and big data technologies (Frith 2015; Kitchin and Dodge 2011). Specifically, this synthesis of RFID and cloud computing infrastructure via corporate partnership provides an alternative discursive positioning of two technologies that are often part of asymmetrical relations of power (Andrejevic 2014). Consequently, it is critical to examine the efforts of Amazon and the NFL to normalize pervasive spatial data collection and analytics to a mass audience by presenting these surveillance technologies as helpful tools for accessing new forms of data-driven knowing and analysis.

scholarly journals Predictive geospatial analytics using principal component regression

2020 ◽  
Vol 10 (3) ◽  
pp. 2651
Author(s):  
Kyilai Lai Khine ◽  
ThiThi Soe Nyunt
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Hybrid Approach ◽  
Principal Component Regression ◽  
Principal Component ◽  
Geospatial Data ◽  
Data Sets ◽  
Mlr Model

Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.

A Review on 3D Spatial Data Analytics for Building Information Models

2019 ◽  
Vol 27 (5) ◽  
pp. 1449-1463 ◽  
Author(s):  
Yu-Wei Zhou ◽  
Zhen-Zhong Hu ◽  
Jia-Rui Lin ◽  
Jian-Ping Zhang
Keyword(s):  
Spatial Data ◽  
Data Analytics ◽  
Information Models ◽  
Building Information ◽  
Building Information Models
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