scholarly journals Geospatial Data Processing for Biometeorology. Mapping the Difference of Oxygen in the Atmospheric Surface

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 19
Author(s):  
Benjamin Arroquia-Cuadros ◽  
Ángel Marqués-Mateu ◽  
Laura Sebastiá ◽  
Pablo Fdez-Arroyabe
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Spatial Data ◽  
Geospatial Data ◽  
Data Sources ◽  
Sources Of Information ◽  
Data Formats ◽  
Data Files ◽  
The Difference ◽  
Scientific Fields

Biometeorology is the field that relates meteorological and climatic variables with humans, animals and the environment in order to be studied jointly with their geographical distribution. The wide variety of data sources and the highly specialised data formats are fundamental issues for users in this area. This paper presents some preliminary results and several underlying technologies used to create a system to manage spatial data. Some sources of information are presented as a basis for biometeorological studies, together with a procedure for downloading and transforming the datasets. The resulting maps and data files derived from this study are useful for data analysis in other scientific fields.

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 Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

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 Analisis Pembiayaan Arrum Pada Usaha Mikro Kecil Di Pegadaian Syariah Kota Palopo

2021 ◽  
Vol 4 (1) ◽  
pp. 38
Author(s):  
Arini Lestari Aris ◽  
Charisma Ekawaty
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Problem Solving ◽  
Data Processing ◽  
Secondary Data ◽  
Data Sources ◽  
Secondary Data Sources ◽  
Processing Techniques

AbstrakPenelitian ini bertujuan untuk mengetahui sistem pelaksanaan pembiayaan Arrum pada Pegadaian Syariah Kota Palopo dan memastikan bahwa benar tidak ada unsur riba dalam pelaksanaannya. Jenis penelitian yang digunakan adalah penelitian kualitatif dengan menggunakan sumber data primer dan sekunder yang diambil dengan wawancara langsung dan observasi, serta dokumen-dokumen pendukung yang ada di Pegadaian Syariah Kota Palopo. Teknik pengolahan data yang digunakan yakni, editing, organizing, dan hasil temuan, sementara untuk analisis data menggunakan pola pikir induktif yang berarti pola pikir yang berpijak pada fakta-fakta yang bersifat khusus kemudian diteliti, dianalisis dan disimpulkan sehingga pemecahan persoalan atau solusi tersebut dapat berlaku secara umum. Hasil temuan yang didapatkan bahwa sistem pelaksanaan yang dilakukan oleh Pegadaian Syariah Kota Palopo telah sesuai dengan syariat islam dan prosedur operasional Pegadaian Syariah dan tidak ada unsur riba di dalamnya.Kata kunci: Pembiayaan, Arrahn (Arrum), Pembiayaan Arrum BPKB AbstractThis study aims to determine the Arrum financing implementation system at the Palopo City Sharia Pawnshop and ensure that there is really no element of usury in its implementation. This type of research is qualitative research using primary and secondary data sources which are taken by direct interviews and observations, as well as supporting documents in the Palopo Sharia Pawnshop. The data processing techniques used are editing, organizing, and findings, while for data analysis using an inductive mindset, which means a mindset that is based on specific facts and then researched, analyzed and concluded so that the problem solving or solution can apply. generally. The findings show that the implementation system carried out by the Palopo City Sharia Pawnshop is in accordance with Islamic Sharia and Sharia Pawnshop operational procedures and there is no element of usury in it.Abstraks dalam bahasa inggis juga diperlukan sebagaimana yang diuraikan sebelumnya.Keywords: Financing, Ar-rahn (Ar-rum), Ar-rum Financing of BPKB.

scholarly journals Komparasi Materi Bacaan Al–Qur'ān antara Riwāyat Hafş Ibn Sulaimān Al-Kūfīy dengan Riwāyat Warsy 'Uśmān Ibn Sa'īd Al-Mişrī dan Implikasinya pada Pembelajaran Baca Al–Qur'ān

2018 ◽  
Vol 9 (2) ◽  
pp. 275
Author(s):  
Rizal Taufik S
Keyword(s):  
Data Analysis ◽  
Research Process ◽  
Data Sources ◽  
Primary Data ◽  
Historical Aspect ◽  
Reading Material ◽  
Library Research ◽  
Teachers And Students ◽  
Similarities And Differences ◽  
The Difference

This study discusses the similarities and differences in Al-Qur'an reading material between riwāyat Hafş Ibn Sulaimān al-Kūfiy and riwāyat Warsy 'Uśmān Ibn Sa'īd al-Mişrī and its implications for Al-Qur'ān reading learning. In implementing this research process, methods are used that are in harmony with the object of research, so the author uses a type of library research and is descriptive, comparative, or content analysis. In line with the type and nature of research, the data sources that I use are primary data sources, in an effort to collect data using the Library Study method. Data analysis used is qualitative data analysis. Based on the results of the study show that there are similarities and differences in the reading material between riwāyat Hafş Ibn Sulaimān al-Kūfiy and riwāyat Warsy 'Uśmān Ibn Sa'īd al-Mişrī as well as its implications for Al-Qur'an reading reading, namely the similarity in pronouncing makhārij al-hurūf and şifat al-surat. However, there are differences in the form of verbs, iśbat, dialect differences (lahjah). Thus the difference in qirā'āt is caused by 2 (two) aspects, namely the historical aspect and the biological aspect. Implications for Al-Qur'an reading learning which includes components such as goals, material, methods, teachers and students, media and evaluation.

scholarly journals EVALUATION OF THE PRESERVATION STATE OF HISTORICAL PENINSULA IN ISTANBUL BASED ON GEOSPATIAL DATA

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 353-357
Author(s):  
Y. Can ◽  
S. Tura ◽  
E. Kudde
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Structural State ◽  
Geospatial Data ◽  
Huge Amount ◽  
World Heritage Sites ◽  
Heritage Sites ◽  
Cultural Assets ◽  
Public Property ◽  
Gis Software

<p><strong>Abstract.</strong> Inventory Project for the Cultural Assets of Istanbul (2015–2019) revealed that there are approximately 35000 historical and cultural assets in Istanbul due to its history with regards to being homeland and capital of many different civilizations. Historical Peninsula (Fatih district) which also contains four World Heritage Sites listed by UNESCO has 30% of the total registered historical assets inventory in Istanbul. Throughout the inventory project for the cultural assets of Istanbul, huge amount of data was collected by site-work with their spatial references. Cultural assets’ database was related with the spatial data on GIS software and it will serve as a tool for various analyses in order to understand and evaluate the situation. Essentially 11 analyses were generated from inquirable geospatial data for Historical Peninsula of Istanbul. Geospatial data is constituted of approximately 140 distinct data-type including location, architectural description, conservation state, materials or cultural era that can be useful for different analyses and also cross-examine such as non-functional assets on public property or structural state of assets which require an urgent intervention. In addition, specific thematic maps and different routes for touristic and cultural purposes can be produced on GIS platforms, based on this study. In this paper, these mentioned studies of the Inventory Project of Istanbul will be described in detail and several case studies generated for the Historical Peninsula will be presented. It is aimed to define a data processing methodology created for cultural heritage by using GIS platforms in order to be evaluated in further projects.</p>

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 COVID-19 data sources: evaluation of map applications and analysis of behavior changes in Europe’s population

Geografie ◽  
2020 ◽  
Vol 125 (2) ◽  
pp. 171-209
Author(s):  
Vít Pászto ◽  
Jaroslav Burian ◽  
Karel Macků
Keyword(s):  
Data Analysis ◽  
Information Sources ◽  
Preventive Measures ◽  
Geospatial Data ◽  
Data Sources ◽  
Behavior Changes ◽  
Tabular Data ◽  
Community Mobility ◽  
Geovisual Analytics

Due to the current situation, and preventive measures taken to tackle COVID-19, it is crucial to keep society well-informed. Besides media and official news, that often include tabular data, it has also become a new standard for information sources to incorporate a map application or geovisualization. This paper offers a comprehensive and systematic overview describing the most prominent and useful map applications and map visualizations. News outlets should place the same importance on data analysis and interpretation as they place on data visualization. This paper emphasizes the role of geospatial data and analysis during the COVID-19 pandemic and aims to provide insights into the topic in order to better understand the consequences caused by the disease. Specifically, the paper deals with the COVID-19 Community Mobility Reports dataset, offering unique information about changes in human activity due to the pandemic. We show how this dataset can be utilized in terms of geovisual analytics and clustering in order to reveal the spatial pattern of such changes in human behavior.

scholarly journals BITOUR: A Business Intelligence Platform for Tourism Analysis

2020 ◽  
Vol 9 (11) ◽  
pp. 671
Author(s):  
Alexander Bustamante ◽  
Laura Sebastia ◽  
Eva Onaindia
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Data Processing ◽  
Data Visualization ◽  
Business Intelligence ◽  
Data Sources ◽  
Data Driven ◽  
Decision Making Process ◽  
The Core ◽  
Processing Data

Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.

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.

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