A Stacked Generalization Framework for City Traffic Related Geospatial Data Analysis

2016 ◽  
pp. 265-276
Author(s):  
Xiliang Liu ◽  
Li Yu ◽  
Peng Peng ◽  
Feng Lu
Keyword(s):  
Data Analysis ◽  
Geospatial Data ◽  
Stacked Generalization

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

scholarly journals Interoperability in planetary research for geospatial data analysis

2018 ◽  
Vol 150 ◽  
pp. 36-42 ◽  
Author(s):  
Trent M. Hare ◽  
Angelo P. Rossi ◽  
Alessandro Frigeri ◽  
Chiara Marmo
Keyword(s):  
Data Analysis ◽  
Geospatial Data

Mobile Technologies for Geographic Response Plans (V2.0)

2017 ◽  
Vol 2017 (1) ◽  
pp. 2017378
Author(s):  
Guillaume Nepveu ◽  
Alain Lamarche ◽  
Stéphane Grenon
Keyword(s):  
Data Analysis ◽  
Power Efficiency ◽  
Large Scale ◽  
Geospatial Data ◽  
Mobile Technologies ◽  
Critical Element ◽  
Gas Industry ◽  
Field Surveys ◽  
External Data ◽  
Internet Connections

Companies in the oil & gas industry must undertake extensive environmental studies for every new project. The case of the Trans Canada East Energy pipeline project illustrates this phenomenon. These studies include large-scale field surveys campaigns that generate a huge amount of raw geospatial data. A key component of this process resides in the analysis of this data in order to create knowledge. In this perspective, while many mobile geospatial collectors are available, we focused on designing a complete mobile GIS. Most field experts are not data management specialists and vice versa. Our goal is to use mobile technology to reduce the gap between field observations and geospatial data analysis. Bringing data analysis capabilities directly in the field is advantageous because it provides useful insights in real-time and it can avoid costly mistakes. We have also focused on enhancing the planning phase prior to the field surveys, such as allowing importation of external data into our system. We encountered many challenges while developing our mobile geospatial solution for geographic response plans. One critical element was the mean of transportation used during the surveys. For instance, airborne surveys using a helicopter requires specific navigational features to be accurate. Another element was the availability of internet connections. We had to permit offline map layer in order to provide high quality satellite imagery. Finally, power efficiency is critical for long field surveys. We had to optimize our mobile application in order to maximize the battery's lifespan while retaining enough features such as the GPS precision. Overall, the challenge was to design a system that would permit monitoring that could take place years after the initial data collection. This affected the way we had to implement data sharing, storage and export.

Geospatial data analysis for DHS programs

2009 ◽  
Author(s):  
Eric Stephan ◽  
John Burke ◽  
Carrie Carlson ◽  
Dave Gillen ◽  
Cliff Joslyn ◽  
...  
Keyword(s):  
Data Analysis ◽  
Geospatial Data

Geospatial Data Analysis: A Review of Theory and Methods

2017 ◽  
Author(s):  
Prof. Rajagopalan S ◽  
Yogalakshmi Jayabal
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Geospatial Data ◽  
Data Generation ◽  
Vast Amount ◽  
Temporal Aspect ◽  
Geospatial Data Collection

A vast amount of data is generated and collected every moment and often, data has a spatial and/or temporal aspect. This increasing data generation and collection is resulting in increasing volume and varying formats of data being collected and the geospatial data collection is no exception. This posses challenges in storing, processing, analyzing and visualizing the geospatial data. This paper discusses the big data paradigm of the geospatial data and presents a taxonomy for analysis of the geospatial data. The existing literature is studied and discussed based on the proposed taxonomy for analysis of geospatial data.

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